SELF-MANAGEMENT AND RELATIONSHIP MANAGEMENT AS DETERMINNTS OF MENTAL HEALTH DISORDERS AMONG INTERNET-ADDICTED UNIVERVITY STUDENTS IN RIVERS STATE, NIGERIA
SELF-MANAGEMENT AND RELATIONSHIP MANAGEMENT AS DETERMINNTS OF MENTAL HEALTH DISORDERS AMONG INTERNET-ADDICTED UNIVERVITY STUDENTS IN RIVERS STATE, NIGERIA
- Details
- Published: 02 February 2023
SELF-MANAGEMENT AND RELATIONSHIP MANAGEMENT AS DETERMINNTS OF MENTAL HEALTH DISORDERS AMONG INTERNET-ADDICTED UNIVERVITY STUDENTS IN RIVERS STATE, NIGERIA
BY
Udeorah, Florence Ashidinma1
Phone: +234-8037080505
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
&
Prof. I.R. Ernest- Ehibudu2
Phone: +234-
DEPARTMENT EDUCATIONAL, PSYCHOLOGY, GUIDANCE AND COUNSELLING, FACULTY OF EDUCATION, UNIVERSITY OF PORT HARCOURT, RIVERS STATE, NIGERIA.
Abstract
This study investigated self-management and relationship management as determinants of mental health disorder among internet-addicted University students in Rivers State. The study adopted correlational research design. The population for this study was 8,700 students in the faculty of education drawn from the three government owned universities in Rivers State, that is, University of Port Harcourt, Rivers State University and Ignatius Ajuru University of Education. The Sample size of 870 was used for the study. The number of respondents who were internet-addicted were determined using the Internet-addicted Scale of one-dimensional Instrument questionnaire adapted from Young (1998) and this totaled 777. The sampling technique used for this study was the random and purposive sampling techniques. The primary data used for this were obtained using a structured questionnaire. The study employed the Emotional Intelligent Inventory (EII), as measure of psychological construct involving self-management and relationship management, while the Generalized Anxiety Disorder (GAD-7) and Becks Depression Inventory (BDI) were used as measure of mental health disorder. The primary data sourced by means of a structured questionnaire were analysed using regression method and analysis of variance (ANOVA). From the regression result, the study found that, relationship management predicted depression (p=.03<.05). on the other hand self-management and anxiety (p=.96>.05), self-management and depression (p=.56>.05) as well as relationship management and anxiety (p=.82>.05) were not significant predictors among internet addicted University students in Rivers State. The study therefore recommends that, counselling psychologist, lecturers and parents should help children in developing and managing healthy relationship as this can help them in controlling depression.
Keywords: Self-management, Relationship management, Depression, Anxiety, internet-addiction.
Introduction
According to Odaci & Çelik, (2013), the impact of internet in our lives has become more and more significant and undeniable. Life without the internet is definitely very challenging and inconveniencing in this age and time. The internet is frequently used for online purchasing, data collection, messaging, communicating with others and so on. The use of the internet has increased enormously and now it seems that every aspect of our lives has been affected by the internet. Although, the internet offers many advantages in this era of global communication, its excessive use can produce many negative consequences.
The American Society of Addiction Medicine (2019) defines “Addiction” as a primary, chronic disease of brain reward, motivation, memory and related circuitry. Also, it is a repetitive, or excessive behavioural syndromes such as sex addition, internet gaming addiction, exercise addiction, shopping addiction among others. According to Griffiths (2007), addiction is biopsychosocial disorder characterized by repeated use or repetitive engagement in a behaviour such as gambling, despite harm to self and others. From the above definitions, addiction is engagement in a repetitive behaviour which is different from dependence on, as in drug addiction. For the purpose of this study, the researcher investigated internet addiction (IA).
In the light of this, internet addiction (IA) could be seen as a disorder is a pattern of excessive and prolonged internet use that results in a cluster of cognitive and behavioural symptoms, including progressive loss of control over gaming, tolerance and withdrawal symptoms, which are analogous to the symptoms of substance use disorders (APA, 2013). Internet addiction has been conceptualized as an uncontrollable desire to use the internet (Jelenchick, Becker, & Moreno 2012). Internet addiction is a compulsive, persistent and uncontrollable use of the internet to such a degree that cessation causes severe emotional, mental or physiological reactions (Qadri Qadri, EsmailiKurane, & Ahmad, 2014). Beard (2005) described internet addiction as an uncontrollable and damaging use of the internet. Young (cited in Tripathi, 2018) defined internet addiction (IA) as an impulse control disorder which does not involve an intoxicant. Hence, internet addiction is a psychological dependence on the internet regardless of the type of activities pursued. It is person and machine interaction. Internet Addiction leads to an impairment of various life functions (Ko et al, 2006, Tripathi, 2018), especially mental health or functioning. Internet addiction is not a disease but is it a psychological and behavioural disorder (Griffiths, 2001).
As noted by Orsal, Orsal, Unsal, & Ozalp, 2013, an internet addict as a person who is not able to set a limit for internet use, using the internet uncontrollably notwithstanding its social, psychological or emotional effects. Furthermore, Orsal et al added that internet addicts usually experience feelings of intense anxiety in situations where their access to internet is restricted. An internet addict is a person who is unable to control his or her use of the internet, which eventually causes psychological, social, school and/or work difficulties in a person’s life (Davis, 2001). It refers to a person who is glued to his or her phone, computer, or other electronic device that has access to the internet more so than the average person does (Odaci & Çelik, 2013). The challenge comes when these activities start to interfere with one’s daily life.
Beard (2005) further explained that just because a person uses the Internet a lot, watches a lot of YouTube videos, shops online frequently, or logs into social media does not mean the person is suffering from internet addiction. As with substance-related disorders, individuals with internet addiction disorder continue to sit at a computer, or with a smart-phone and engage in different activities despite neglect of other activities. Such individuals typically devote eight (8) ten (10) hours or more per day to this activity and at least thirty (30) hours per week. If they are prevented from using a computer or smartphone, they become agitated and angry. They often go for long periods without food or sleep. Normal obligations, such as school or work, or family obligations are neglected. Pontes and Patrão (2013) stressed that this lack of control over the use of the internet by internet addicts tends to affect their daily activities, psychological, mental and emotional states, school performance and social interactions.
The term emotional intelligence refers to individual differences in perceptions, processing, managing and utilization of emotional information. According to Tyagi and Gautam (2017), “emotional intelligence is the ability of any person to understand his/her own emotions and to differentiate between different feelings”. It can be seen as the ability to manage and adjust emotions to achieve the required goals. It can be also said that a person who has high emotional intelligence is able to understand the negative impact of emotions in peoples’ minds, bodies, relationships and their capabilities to achieve something. It has also been defined as “the ability to monitor one’s own and others’ feelings and emotions, to discriminate among them and to use this information to guide one’s thinking and actions” (Salovey and Mayer, 1990). It also involves an individual’s perception of others emotions. An emotion is a complex psychological state that involves three (3) distinct components: subjective experience, physiological response and behavioural or expressive response (Hockenbury and Hockenbury, 2007).
Self-management is an important component of Emotional Intelligence. An individual who can manage his/her emotions will be able to manage his/her entire being. Emotional self-management is the ability of an individual to control his or her emotions in a manner appreciable to the society in which he or she lives. Lack of emotional self-management can lead to frustrations due to anger and rage. Being a person of integrity, whom people can trust, reflects the Emotional Intelligence of the individual. One’s deeds determine whether he is trustworthy or not. People with high levels of Emotional self-management will manage their personal matters such that they will be dedicated to their work. The quality of a person to adapt to a situation easily as well as to be optimistic in all situations substantiates that the individual is emotionally intelligent. One can manage his activities very well if he or she has a desire to achieve something. An orientation to achieve something acts as a motivation for a better performance. The readiness of an individual to initiate an action is a clear evidence of his Emotional management level. A person with high emotional regulation likes to initiate an action and to be the source of that change. A situation where an individual experiences mental health disorders like depression, withdrawal among others depicts the fact that he or she is low on self-management. Most researchers have found that internets have issues with emotional intelligence, does it include the component of emotional self-management. This study would find out if self-management is one of the areas of mental health challenges of internet addicts.
From the study of Li, et al (2021), on the Relationship Between Self-Control and Internet Addiction Among Students: A Meta-Analysis, with a purpose to: (a) synthesize the results of past studies on the relation between self-control and Internet addiction among students (10–22 years old) and (b) identify factors that influence this relationship. Specifically, this meta-analysis (a) calculates the overall effect size of the link between self-control and Internet addiction and (b) determines whether culture, age, gender, Internet addiction measures or publication year moderated this link. This meta-analysis included Chinese and English-language publications during January 2002 to September 2020. Using the random-effects meta-analysis of Pearson product-moment coefficients (r) with Fisher’s z-transformation and tested for moderation with the homogeneity tests, result showed that self-management (self-control) was negatively related to Internet addiction (r = 0.362). Self-management was negatively linked to Internet addition.
From the study of Far et al., (2014) on relationship between the components of emotional intelligence and internet addiction of students in Kharazmi University, the researchers employed a descriptive, correlative method and the sample comprised 400 students who were selected using stratified sampling form all the faculties located in Kharazmi University. The research tools included Internet Addiction test (IAT) designed and developed by Young and The Schutte Self-Report Emotional Intelligence Test (SSEIT). Results show that emotional self-management is significantly, negatively correlated internet addiction (r = -0.23).
Ahmed and Najeh (2017) examined self-control and its relationship with the internet addiction among a sample of Najran University students. To achieve the objectives, the following tools were used: Self-control Scale (Mezo, 2009) that was translated and adjusted by the authors and Internet Addiction Scale (Kimberly Young, 1996). After testing the validity and reliability of the tools to be appropriate for the Saudi environment, they were applied to a sample of (284) students who were randomly selected from various Colleges, Najran University. Results showed that the participants’ self-control was low, while Internet Addiction was high. In addition, there is a positive correlation between self-control and internet addiction. There are statistically significant differences in self-control among NU students due to gender in favor of males. There are no statistically significant differences of Internet Addiction among students, but there are differences in Self-Control among them due to college, in favor of Scientific Colleges’ students. There are no statistically significant differences in Internet Addiction due to college.
Ahmet, Serhat, Nihan, Recep & Ümit (2015) examined the relationship of self-control/management and Internet addiction. Participants were 309 university students who completed a questionnaire package that included the Online Cognition Scale and the Self-control and Self-management Scale. The relationships between self-control/management and Internet addiction were examined using correlation analysis and multiple regression analysis. According to results Internet addiction were predicted negatively by self-monitoring, self-evaluating, and self-reinforcing. Results were discussed in the light of literature.
Yang, et al., (2014) examined Effects of Self-efficacy and Self-control on Internet Addiction in Middle School Students from J city in South Korea: A Social Cognitive Theory-Driven Focus on the Mediating Influence of Social Support. The participants in the study were 119 middle school students in J city. The measurements included a self-efficacy scale, a self-control scale, a social support scale, and the Internet Addiction Scale for Youth. Data were analyzed using the independent t-test or Mann-Whitney U test, one-way analysis of variance, the Scheffé test, Pearson correlation coefficients, and multiple-regression using SPSS version 22.0. Mediation effects were analyzed by the Sobel test and Baron and Kenny's hierarchical analysis technique. Significant correlations were found among self-efficacy, self-control, and internet addiction. Social support had partial mediating effects in the relationship between self-efficacy and internet addiction, as well as in the relationship between self-control and internet addition. In order to prevent internet addiction, the promotion of interactions among peers, which is a component of social support, is particularly important. It is also necessary to promote face-to-face activities that can strengthen relationships. The findings suggest that intensifying social support may help reduce the level of internet addiction in middle school students.
Relationship management domain contains competencies that have the most direct effect on interactions with other people. In a fundamental sense, the effectiveness of one’s relationship skills hinges on one’s ability to attune to or influence the emotions of another person. Individuals with strong relationship management are typically team players. Rather than focus on their own success, they assist others develop and shine. They can manage disputes, are excellent communicators, and are masters at building and maintaining relationships. The ability to manage people and relationships is very important in all leaders, so developing and using your emotional intelligence can be a good way to show others the leader inside of you. Emotionally intelligent people are aware of needs and problems in their society where they live in. In a society, it is easy for an individual to make relation with others; but to maintain that relation for a long time requires skill. People with high emotional intelligence level have the ability to maintain good relationship with others. Understanding the needs of others and helping them to develop is a characteristic of emotionally intelligent people. Giving help in all the possible ways to develop others is a quality which facilitates good relationship with others. On relationship management, Karimzadeh, (2015) on Investigating the relationship between Internet addiction and strengthening students' social skills, adopting correlation research design. using a multi-stage cluster sampling, 345 Undergraduate students of Islamic Azad University of Shahrekord were selected as sample population. The instruments used for the study were two questionnaires, one for Internet addiction and the other for students' social skills. According analysis, the significant level is equal to 0.000, and comparable with the amount of allowable error, 0.05; the hypothesis is rejected with 95% confidence. It means that there is a meaningful relationship between internet addiction and strengthening of relationship management. So according to the amount and sign of the Pearson correlation coefficient given in the above which is equal to -0.185, it can be concluded that this relationship is the reverse. Based on the results, the data was analyzed using descriptive statistics. The statistical analysis indicates that the level of confidence is 95 percent and the level of significance is 0.000. Comparing these results with the amount of allowable error, which is 0.5, the hypothesis is rejected. Therefore, there is a meaningful relationship between internet addiction and strengthening of relationship management. So, the hypothesis is confirmed. According to the amount and sign of the Pearson correlation coefficient which is equal to -0.185, it can be concluded that this relationship is the reverse.
Chou, et al (2017), in their study on Social skills deficits and their association with Internet addiction and activities in adolescents with attention-deficit/hyperactivity disorder, a total of 300 adolescents from two Medical Centres in Kaohsiung, Taiwan within the age of 11 and 18 years, made the sample for the study. Parent-reported Social Skills and Behaviors Checklist for Children and Adolescents (SSBCA-C) was used to measure the participants’ social skills deficits (Meng, 2004) and Chen Internet Addiction Scale (CIAS) was used to assess the severity of the participants’ self-reported Internet addiction levels. Results revealed that relationship management skills deficits were significantly associated with an increased risk of Internet addiction after adjustment for the effects of other factors [odds ratio (OR) = 1.049, 95% confidence interval (CI) = 1.030–1.070]. Furthermore, results indicated that participants who engaged in online gaming (p = .007), BBS (p = .033), and watching movies (p = .026) had more severe relationship management skills deficits than those who did not participate in these Internet activities.
Depression and anxiety on a lighter note are psychological disorders that are suspected to be rampant among internet-addicted University students to be discussed extensively. Depression has been conceptualized as mental state of the mind producing serious mood swings, loss of interest or pleasure, decreased energy, feelings of guilt or low self-worth, disturbed sleep or appetite and poor concentration (World Health Organization,2012). Anxiety on the other hand, is seen as an unpleasant, complex and variable pattern of behavior which individuals show when reacting to internal (thought and feelings) or external (environmental situation) stimuli (Oladele, 2005) It is against this background that the researcher is interested in examining the relationship between psychological constructs of emotional intelligence, subjective well-being, self-esteem as determinants of anxiety and depression (mental health disorder) among internet addicted university students in Rivers State.
Without doubt, internet addiction among university students is a major problem which can cause serious health risks, pose academic challenges, abnormal heart rhythms, irritability and sensory disturbances. Although, most students who are addicted to the internet feel that being online helps them reduce tension, frustration, boredom and sometimes help them stay awake all night or increase their mental alertness. However, experiences of some internet addicts have shown that excessive use and spending a lot of time on the internet carries a high price tag which often leads to anxiety, insomnia and depression and so on. Internet addiction interferes with a person’s normal life causing the person to spend more time in solitary seclusion and spending less time with real people in their lives which often make others view them as socially awkward.
It is believed that major consequences of internet addiction among university students are poor academic performance, truancy and increased school dropout. From the foregoing therefore, the researcher is motivated to examine the relationship of some psychological constructs (self-management and relationship management) and their relationship with anxiety and depression among internet addicted university students in Rivers State.
Aim and Objectives of the Study
The aim of the study is to examine psychological constructs as determinants of mental health disorders among internet addicted University students in Rivers State, Nigeria. The study would achieve the following specific objectives:
- determine the extent to which self-management predicts anxiety (mental health disorder) among internet- addicted university students in Rivers State.
- determine the extent to which self-management predicts depression (mental health disorder) among internet- addicted university students in Rivers State.
- determine the extent to which relationship management predicts anxiety (mental health disorder) among internet- addicted university students in Rivers State.
- determine the extent to which relationship management predicts depression (mental health disorders) among internet- addicted university students in Rivers State.
Research Questions
The following research questions guided this study;
- To what extent does self-management predict anxiety (mental health disorder) among internet-addicted university students in Rivers State.
- To what extent does self-management predict depression (mental health disorders) among internet- addicted university students in Rivers State.
- To what extent does relationship management predict anxiety (mental health disorder) among internet-addicted university students in Rivers State.
- To what extent does relationship management predict depression (mental health disorder) among internet-addicted university students in Rivers State.
Hypotheses
The following null hypotheses would be tested in this study at 0.05 level of significance;
- Self-management does not significantly predict anxiety (mental health disorder) among internet-addicted university students in Rivers State.
- Self-management does not significantly predict depression (mental health disorder) among internet-addicted university students in Rivers State.
- Relationship management does not significantly predict anxiety (mental health disorder) among internet-addicted university students in Rivers State.
- Relationship management does not significantly predict depression (mental health disorder) among internet- addicted university students in Rivers State.
Methodology
This research adopted the correlational survey research design. The population of this study comprised of all eight thousand seven hundred (8,700) undergraduate students in the faculty of Education in the three government owned universities in Rivers State, Nigeria namely, University of Port Harcourt, Rivers State University and Igntuius Ajuru University of Education, Rumuolumeni. The Sample size used for this study was seven hundred and seventy-seven (777) which was derived from ten (10) percent (%) of the population which yielded a total of 870. Due to the specificity of the study which targets internet addicted students in the three public universities in Rivers State (University of Port Harcourt, Rivers State University and Ignatius Ajuru University of Education), eight hundred and seventy (870) designed questionnaire were administered. Proportional method was applied in administering the eight hundred and seventy (870) questionnaire to the three universities selected for this study taking into consideration, the student population of each of the Universities. The rationale for this was that, it is possible that not all students that make up the population and sample size may be internet addicts. The eight hundred and seventy (870) questionnaire administered were then analysed to determine the number of students that are internet addicts. The purposive sampling technique was appropriate for selecting students who possessed the characteristics to be studied. This was achieved by distributing copies of the Internet Addiction Scale (IAS). In distributing the questionnaire among the three universities, the proportional allocation technique was applied.
For data collection in the present study, three distinct instruments were used which were titled Internet Addiction Scale (IAS), Emotional Intelligence Inventory (EII), and the Mental Health Disorder Inventory (MHDI) (Anxiety and Depression). The IAS is a one-dimensional Instrument questionnaire adapted from the Young (1998) Internet Addiction Test. The instrument is divided into sections A and B and so on. Section A of the instrument was titled Internet Addiction Scale (IAS), is was a 15-item scale constructed on a four-point Likert Scale of Regularly, Sometimes, Rarely and Never which were scored 4, 3, 2, and 1 point(s) respectively.), This instrument was used for screening purposely and only to identify students who were internet addicted. To establish students with internet addiction, the instrument was given an average score of 30. Since the instrument has a maximum score of 60, an average of 30 was taken. Hence, any students who scored up to 30 and above in the internet addiction scale, were adjudged to be addicted to the internet and vice-versa. The IAS is a one-dimensional Instrument questionnaire adapted from the Young (1998) Internet Addiction Test.
The second instrument was the Emotional Intelligence Inventory which was adapted from Goleman (1995). The instrument was used to assess the emotional intelligence of respondents. The instrument was a multidimensional instrument which assesses the two components of emotional intelligence as conceptualized by Goleman (1995) namely self-awareness and social awareness. The instrument was made up of 10 items. The instrument was constructed using the modified four-point Likert Scale of Strongly Agree, Agree, Disagree, and Strongly Disagree which were scored 4, 3, 2, and 1 point(s) respectively. Scores from each component as well as the total score of the instrument were used for subsequent data analysis.
The final instrument titled Mental Health Disorder Inventory (MHDI) was made up of both adopted Generalized Anxiety Disoder-7 and adapted Beck’s Depression Inventory combined. Generalized Anxiety Disorder-7 (GAD-7) by Splitzer and colleagues (2006). The adopted instrument contains 7 items. This instrument was used to elicit responses on Generalized Anxiety. The GAD-7 is scored and calculated by assigning scores of 0, 1, 2, and 3 to the response categories, respectively, of “not at all,” “several days,” “more than half the days,” and “nearly every day.”. GAD-7 total score for the seven items ranges from 0 to 21. 0–4: minimal anxiety 5–9: mild anxiety 10–14: moderate anxiety 15–21: severe anxiety. The scale is designed with response option of the four-point Likert rating scale of Not at All, Several Days, More Than Half the Days, Nearly Every Day. While a 13 items of the Beck’s Depression Inventory (BDI) was used to measure the level of depression among respondent. It is designed on a 4-point Likert scale of Strongly Agree (SA), Agree (A), Disagree (D) and Strongly Disagree (SD) which were scored 4, 3, 2, and 1 point(s) respectively.
Content and construct validities of the research instruments were determined by giving them to experts in Test and Measurement in for vetting. The reliability indices of the instruments were determined using the Cronbach Alpha method of internal consistency with reliability coefficients of .73 for Internet Addiction Scale and .80 for Emotional Intelligent Inventory (.80) and .81 for Mental Health Disorder Inventory respectively.
Eight hundred and seventy (870) copies of the instruments were distributed and retrieved after the respondents gave their responses. Responses from the research questions were analyzed and answered with simple and multiple regression while the hypotheses tested at 0.05 level of significance were analyzed with associated t-test and ANOVA.
Results
Research Question 1: To what extent does Self-management predict anxiety (mental health disorder) among internet addicted university students in Rivers state?
Table 1: Simple regression analysis showing R correlations values for predicting Self-management and anxiety (mental health disorder) among internet addicted university student
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.002 |
.000 |
-.001 |
4.936 |
From the analysis in Table 1, the relationship between self-management and anxiety is .002, R2 value is .000, and Adjusted R2 is -.001 while the standard estimate value is 4.936. Therefore, the R2 value signifies that about 0% of self-management predict anxiety (mental health disorder) among internet addicted university students in Rivers state.
Hypothesis One: Self-management does not significantly predict anxiety (mental health disorder) among internet addicted university students in Rivers State.
Table 2: Showing associated ANOVA of Self-awareness as predictor of anxiety (mental health disorder) among internet addicted university students in Rivers State
|
Model |
Sum of Square. |
df |
Mean Sqare. |
F |
Sig. |
Remark |
|
|
|
Regression |
.064 |
1 |
.064 |
.003 |
.96 |
Not significant |
|
Residual |
18885.465 |
775 |
24.368 |
|
|
|
|
|
Total |
18885.529 |
776 |
|
|
|
|
|
The calculated F value in Table 2 is .003 while the sig value is .959. Therefore, since the sig (p = .959 > 0.05) is greater than the chosen alpha of 0.05 at 775 degrees of freedom, the null hypothesis is accepted meaning that self-management does not predict anxiety (mental health disorder) among internet addicted university students in Rivers State.
Research Question 2: To what extent does Self-management predict depression (mental health disorder) among internet addicted university students in Rivers state?
Table 3: Simple regression analysis showing R correlations values for predicting self-management and depression (mental health disorder) among internet addicted university student
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.021a |
.000 |
.-001 |
7.915 |
From the analysis in Table 3, the relationship between self-management and depression is .021, R2 value is .000, and Adjusted R2 is -.001 while the standard estimate value is 7.915. Therefore, the R2 value signifies that about 0% of self-management predict depression (mental health disorder) among internet addicted university students in Rivers state.
Hypothesis Two: Self-management do not significantly predict depression (mental health disorder) among internet addicted university students in Rivers State.
Table 4: Showing associated ANOVA of Self-awareness as predictor of anxiety (mental health disorder) among internet addicted university students in Rivers State
|
Model |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
Remark |
|
|
|
Regression |
21.321 |
1 |
21.321 |
.340 |
.56 |
Not significant |
|
Residual |
48549.570 |
775 |
62.645 |
|
|
|
|
|
Total |
48570.891 |
776 |
|
|
|
|
|
The calculated F value in Table 4 is .340 while the sig value is .560. Therefore, since the sig (p = .560 > 0.05) is greater than the chosen alpha of 0.05 at 775 degrees of freedom, the null hypothesis is accepted meaning that self-management does not predict depression (mental health disorder) among internet addicted university students in Rivers State.
Research Question 3: To what extent does Relationship-management predict anxiety (mental health disorder) among internet addicted university students in Rivers state?
Table 5: Simple regression analysis showing R correlations values for predicting relationship-management and anxiety (mental health disorder) among internet addicted university student
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
|
|
|
|
|
1 |
.008a |
.000 |
-.001 |
4.936 |
From the analysis in Table 5, the relationship between relationship-management and anxiety is .008, R2 value is .000, and Adjusted R2 is -.001 while the standard estimate value is 4.936. Therefore, the R2 value signifies that about 0% of relationship-management predict anxiety (mental health disorder) among internet addicted university students in Rivers state.
Hypothesis Three: Relationship-management does not significantly predict anxiety (mental health disorder) among internet addicted university students in Rivers State.
Table 6: Showing associated ANOVA of Relationship-management as predictor of anxiety (mental health disorder) among internet addicted university students in Rivers State
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
Remark |
|
|
|
Regression |
1.335 |
1 |
1.335 |
.055 |
.82 |
Not significant |
|
Residual |
18884.193 |
775 |
24.367 |
|
|
|
|
|
Total |
18885.529 |
776 |
|
|
|
|
|
The calculated F value in Table 7 is .055 while the sig value is .815. Therefore, since the sig (p = .815 > 0.05) is greater than the chosen alpha of 0.05 at 775 degrees of freedom, the null hypothesis is accepted meaning that relationship-management does not predict anxiety (mental health disorder) among internet addicted university students in Rivers State.
Research Question 4: To what extent does relationship- management predict depression (mental health disorder) among internet addicted university students in Rivers state?
Table 7: Simple regression analysis showing R correlations values for predicting relationship-management and depression (mental health disorder) among internet addicted university student.
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.008 |
.000 |
-.001 |
4.936 |
From the analysis in Table 7, the relationship between relationship-management and depression is .008, R2 value is .000, and Adjusted R2 is -.001 while the standard estimate value is 4.936. Therefore, the R2 value signifies that about 0% of relationship-management predict depression (mental health disorder) among internet addicted university students in Rivers state.
Hypothesis Eight: Relationship-management does not significantly predict depression (mental health disorder) among internet addicted University students in Rivers State.
Table 8: Showing associated ANOVA of Relationship-management as predictor of depression (mental health disorder) among internet addicted university students in Rivers State
|
Model |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
Remark |
|
|
|
Regression |
294.235 |
1 |
294.235 |
4.72 |
.03 |
Significant |
|
Residual |
48276.656 |
775 |
62.292 |
|
|
|
|
|
Total |
48570.891 |
776 |
|
|
|
|
|
The calculated F value in Table 8 is 4.723 while the sig value is .030. Therefore, since the sig (p = .030 < 0.05) is less than the chosen alpha of 0.05 at 775 degrees of freedom, the null hypothesis is rejected meaning that relationship-management does predict depression (mental health disorder) among internet addicted university students in Rivers State.
Discussion of Findings
Self-management independently and anxiety (mental health disorder) among internet addicted university students in Rivers State.
The result showed that self-management does not predict anxiety as a mental health disorder among internet addicted university students in Rivers state. That is self-management as an independent variable alone cannot be used to predict anxiety among internet addicted university students. The results of this study have shown that the ability of undergraduate students in Rivers State to control their emotions and motivate themselves in all situations or when going through difficult times can translate into their capacity to overcome mental disorders such as anxiety. Hence, the null hypothesis is accepted. The result of the study further means whether individuals are able to manage themselves or not, this has no significant influence on their level of anxiety. This finding of the study may come because the respondents who have the ability to manage themselves may still find it difficult in controlling their level of anxiety. The fact revealed that whether they are good self-managers or not, it cannot guarantee their level of anxiety. This result may come because the respondents have observed irrespective of an individuals’ self-management ability dome of them stull do not have the ability to moderate their level of anxiety and vice-versa. This finding means that individuals will experience anxiety but not depending on their level of self-management ability. It has also implied that whether individuals have self-management or not, this will and cannot determine their feeling of anxiety and vice-versa. Hence, the null hypothesis was accepted. The result of the study is surprising to the researcher because she is fully aware that self-management as a virtue cannot determine the level of anxiety that an individual’s can pass through. This significant relationship between self-management and mental disorder is consistent and corroborates with the findings of Antinienė and Lekavičienė (2017). In the work by Antinienė and Lekavičienė (2017), they found insignificant relationship between self-management and anxiety.
Self-management independently and depression (mental health disorder) among internet addicted university students in Rivers State.
The result showed that self-management does not predict depression as a mental health disorder among internet addicted university students in Rivers state. That is self-management as an independent variable alone cannot be used to predict depression among internet addicted university students. This further implies that whether individuals are able to manage themselves or not, this has no significant influence on their level of depression. The finding of the study may come because the respondents who have the ability to manage themselves may still find it difficult in controlling their level of depression. The fact revealed that whether they are good self-managers or not, it cannot guarantee that they be able to manage depression. This result may come because the respondents have observed irrespective of an individuals’ self-management ability that some of them still do not have the ability to moderate their level of depression and vice-versa. This finding means that individuals will experience depression but not depending on their level of self-management ability. It has also implied that whether individuals have self-management ability or not, this will and cannot determine their feeling of depression and vice-versa. Hence, the null hypothesis was accepted. The result of the study is surprising to the researcher because she is fully aware that if an individual has self-management ability, then they as well can detect their level of anxiety that. The results of this study are in variance with those of Ciarrochi, Deane and Anderson (2002), Brown and Schutte (2006) and Antinienė and Lekavičienė (2017). The difference in findings could lie in terms of geography as the studies were carried out in Lithuania, a country more developed than Nigeria and with high consciousness of mental disorder, diagnosis and treatment.
Relationship-management independently and anxiety (mental health disorder) among internet addicted university students in Rivers State.
The result showed that relationship-management does not predict anxiety as a mental health disorder among internet addicted university students in Rivers state. This implies that relationship-management as an independent variable alone cannot be used to predict anxiety among internet addicted university students. From the result of table, relationship management is not a predictor of anxiety among university students in Rivers State. Therefore, lower level of anxiety associated with internet addicts in Rivers State is not associated with the ability to get along with others or being sensitive to the feelings of others. The result of the study further means whether individuals are able to manage others or not, this has no significant influence on their level of anxiety. As observed in other findings, the present result may others may still find it difficult in controlling their level of anxiety. The fact revealed that whether they are good relationship or not, it cannot guarantee their level of anxiety. This result may come because the respondents have observed irrespective of an individuals’ ability to manage others, some of them still lack in the ability to moderate their level of anxiety and vice-versa. This finding means that individuals will experience anxiety but not depending on their ability to manage others. The result also implies that whether individuals have relationship management ability or not, this will and cannot determine their feeling of anxiety and vice-versa. Hence, the null hypothesis was accepted. The result of the study is surprising to the researcher because she is fully aware that relationship-management cannot determine the level of anxiety that an individual’s can pass through. This finding tends to mirror the situation in developing countries and Nigeria in particular, as the issues of mental disorder in the form of anxiety are hardly discussed by students among their peers and the level of awareness of these mental disorders is quite low. The findings of this study contradict those of Antinienė and Lekavičienė (2017) and Lanciano, Curci, Kafetsios, Elia and Zammuner (2012) whose work revealed that relationship management has a negative and significant relationship with anxiety and that anxiety is associated with the inability to be sensitive to the emotions of another person.
Relationship-management independently and depression (mental health disorder) among internet addicted university students in Rivers State.
The results showed that relationship-management independently contribute to depression (mental health disorder) among university students in Rivers State. This implied that relationship-management independently do predict depression (mental health disorder) among. From the result of table, relationship management is a predictor of depression among University students in Rivers State. Therefore, lower level of depression associated with internet addicts in Rivers State is associated with the ability to get along with others or being sensitive to the feelings of others. The result of the study further means individuals who are able to manage others may experience depression from one point or the other. As observed, the present result revealed that for those who are good relationship managers, it may guarantee their level of depression. This result may come because the respondents have observed that an individuals’ ability to manage others has a direct effect on their ability to moderate their level of depression and vice-versa. Hence, the null hypothesis was accepted. The result of the study is not surprising to the researcher because she is fully aware that relationship-management can determine the level of depression that an individual experience.
The findings of this study corroborate with those of Lanciano, Curci, Kafetsios, Elia and Zammuner (2012) who found that relationship management significantly predicts depression.
Conclusion
This study examined the relationship between some social constructs (self-management and relationship management and mental health disorders (anxiety and depression) among internet addicted University students in Rivers State. In summary, the results of the study showed that relationship management is a predictor of depression. Similarly, it was discovered that, self-management is not determinants of either anxiety or depression among internet addicted University students in Rivers State. The study therefore concludes that.
Recommendations
Based on the findings and the study outcomes, the following recommendations were made:
- Research finding one revealed that self-management is not a significant predictor of anxiety. From here, it is recommended that every individual especially internet addict should be encouraged to understand themselves by counsellors and therapist as it is perceived that this may help them in managing their level of anxiety.
- From research finding two it is recommended that though self-management is not a significant predictor of depression, counselling and psychologist should as well encourage individuals especially internet addicts to learn how to manage themselves and understand their emotion as this is suspected by the researcher to relate with their level or extent of depression.
- Research finding three shows that relationship management is not a significant predictor of anxiety. Based on this, it is equally recommended that individual should learn how to manage themself and relationships but should not look up to that as an avenue towards being able to control or reduce their level of anxiety.
- Research findings four showed that relationship-management is a significant predictor of depression. From this finding, it is equally recommended that counselling psychologist, lecturers and parents should help children in developing and managing healthy relationship as this can help them in controlling depression.
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