Socio-Demographic Differences in Subjective Well-Being among Students in Faith-Based Secondary Schools in Kiminini Sub County, Trans Nzoia County, Kenya

Socio-Demographic Differences in Subjective Well-Being among Students in Faith-Based Secondary Schools in Kiminini Sub County, Trans Nzoia County, Kenya

Philomena Momanyi Moraa and Ignatius Musambai

Masters’ Students, School of Arts and Social Sciences, Tangaza University College, Karen, Nairobi

The Catholic University of Eastern Africa

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7011042

Received: 28 October 2023; Accepted: 04 November 2023; Published: 04 December 2023

ABSTRACT

The study sought to investigate socio-demographic differences in subjective well-being among secondary students from faith-based schools in Kiminini Sub-County, Kenya. A quantitative research design was used for data collection and analysis. The study used a correlational research survey. A sample size of 401 participants was determined using Yamane (1967) formula. Simple random and stratified proportionate sampling methods were employed to select participants for the study. SPSS Version 23 was utilized to analyze the data, which included descriptive and inferential statistics. The Religious/Coping Theory and the Life Circumstance Theory guided the study. The study found significant differences between gender t (399) =5.217, p=.000 and type of school f (2, 399) =22.687, p=.000 with respect to subjective well-being of the participants. On the other hand, there were no significant differences between age F (5, 396) =.840, p=.568, religious background F (6, 395) =.520, p=.761 and class level, F (3, 398) =2.886, p=.057) in relation to subjective well-being among the participants of the study. The study concluded that type of school and gender determined students’ level of subjective well-being. The study recommended that teachers, administrators and stakeholders in education sector in Kiminini Sub-County, Kenya, should ensure that schools are conducive places that facilitates students’ general well-being.

Keywords: Socio-Demographic Characteristics, Subjective Well-Being, Secondary School Students, Faith-Based Secondary Schools.

INTRODUCTION

Subjective well-being is one of the key factors in creating and maintaining productive and healthy societies (Das et al., 2020). This is because it comprises components such as satisfaction with life, positive affect and low levels of unpleasant affect which makes life bearable (Diener et al., 2002). Researchers such as Upasna (2010) have supported these claims by stating that subjective well-being leads to longer life, creativity, collaboration, positive interpersonal relationships and capacity to cope up with challenges. According to Upasna’s insights, individuals are termed to have high levels subjective well-being if they are contented, happy and experiencing low levels of unpleasant feelings. Conversely, people can be said to have low levels of subjective well-being if they frequently experience unpleasant feelings and they are dissatisfied with life (Diener et al., 2002). In general, individuals with high levels of subjective well-being are likely to flourish in different life domains.

However, in spite of the above findings, scholars such as Shiah et al. (2016) have raised questions with regard to the influence of demographic characteristics on levels of subjective well-being among people. Agrawal et al. (2011) for instance, established that age (β=-.228, t=-.6.970, p=.000) , gender (β=-.069, t=-2.105, p=.036), level of education (β=079, t=2.196, p=.028) and individuals’ income (β=.079, t=2.185, p=.029) were significant determinants in positive affect and life satisfaction of adults in Bangalore, India. This means that individuals’ levels of subjective well-being are likely to be different in terms of age, gender, education level and income. In this paper, the researchers attempted to review some studies which endeavoured to investigate the association between demographic traits and Subjective well-being.

A study done by Santos et al. (2012) among 969 undergraduate Filipino students found out a gender difference in life satisfaction of t = 1.107, p < 0.05, among male students  (N = 469 M = 25.3822, SD = 5.3785) and female students (N = 500, M = 23.8586, SD = 5.0909) in the university. As per Santos’ study, it seems that male students were more satisfied with their lives than female students. The study also established that female students scored slightly higher on negative affect (Negative affect =17.8404, SD = 6.6081) as compared to male students (Negative affect =12.4196, SD = 6.6394). These variations indicated the influence of demographic factor such as gender on individuals’ well-being.

Similarly, Yue et al. (2017) conducted a study among 5648 university students aged 17 to 29 years in China. The study found out that female participants scored significantly higher than male participants on subjective well-being (t=-.10.16, p<.001), optimism (t=-.5.84, p<.001) and lastly gratitude (t=-13.54, p<.001). As per these findings, it appears that women respondents encountered and reported positive affect, optimism and optimism which in turn contributed to their well-being.

Dang and Sukontamarn (2020) carried out a study among 2,571 elderly individuals from Vietnam and established that educated participants were more happier (β=3.849, Std Error=2.200) than less educated elderly participants (β=2.025, Std Error=0.994). The study also noted that male participants within age bracket of 70-79 years old were happier (β=0.727, Std Error=0.204)) as compared to their counterparts those within age bracket of 80 years and above (β=0.523, Std Error=0.177). The findings of Dang and Sukontamarn postulate that educated individuals in Vietnam experienced life satisfaction and less negative emotions.

In Africa, Rishworth et al. (2020) involved 470 elderly people from Uganda to investigate the social factors influencing subjective well-being. The results demonstrated that age had a significant, direct and negative effect on subjective well-being (β = 0.42, p = 0.01). In other words, findings of Rishworth and associates posits that increase in age led to decline in levels of subjective well-being among elderly people. It seems that the determinants of subjective well-being among elderly individuals in Uganda were not addressed.

Similarly, Calys-Tagoe et al. (2014) conducted a study among 4,724 adults from Ghana to investigate determinants of subjective well-being among adult Ghanaians. The study found out that low levels of education and low income were both associated with low levels of subjective well-being (OR=0.47, CI: =0.37 – 0.60 and OR= 0.304, CI:= 0.22 – 0.42 respectively). Considering these findings, it implies that income and education are significant determinants of subjective well-being among individuals in Ghana.

In Kenya, Juliane and Mudi (2018) carried out a study among 291 female employees from the county of Kakamega to examine the relationship between access to education and subjective well-being. The findings revealed that there was an association between access to education and female employees’ well-being (t=4.253, p=.000). Relatedly, Thuku (2016) established that women experienced high levels of quality and satisfying life than men among retirees from Nyeri County, Kenya. According to Thuku’s findings, it is clear that female respondents maintained high levels of happiness and contentment even after they stopped engaging in government duties and responsibilities.

In general, reviewed literature suggests that socio-demographic characteristics determine subjective well-being among persons (Agrawal et al., 2011; Juliane & Mudi, 2018; Rishworth et al., 2020; Rodrigues et al., 2017). Similarly, theorists such Diener et al. (1999) have contended that individuals’ general well-being is highly influenced by personal factors such as gender, age, personality, economic status and social structures. Nevertheless, even though this reality seems to be intensely addressed by previous studies, literature review indicated that limited studies have been conducted on this topic especially among secondary school students from faith-based learning centres in Kiminini Sub-County, Kenya. Considering that people are constantly seeking well-being in life, it was therefore necessary to examine these variables with a population of students from faith-based secondary schools. This is significant in helping young people and their instructors be aware of factors that are likely to affect one’s well-being and how to handle them. Thus, the objective of this study was to investigate socio-demographic differences in subjective well-being among students in faith-based secondary schools in Kimimini Sub-County, Kenya.

METHODOLOGY

This study was conducted among students of faith -based secondary schools in Kiminini Sub- County in Trans Nzoia County, Kenya.  The study adopted a quantitative research method. Descriptive statistical operations were used to analyze and summarize demographic characteristics of participants. Inferential statistics such as One-Way ANOVA and T-tests were used to investigate the demographic differences in subjective well-being among the respondents.

A sample size of  401 students was arrived at using Yamane’s (1967), formula. The study used multi-stage sampling method. That is, simple random selection was used to choose seven schools from Kiminini Sub-County’s seven wards, namely Kiminini, Sikhendu, Sirende, Waitaluk, Nabiswa, Hospital, and Milimani. In the next stage, the researcher employed proportionate sampling technique to select the sample size of the study from the research sites. The sample size included both male and female students from the faith-based schools.

The study used  Prayer Type Scale (Poloma & Pendleton, 1989) and the Oxford Happiness Scale (Hills & Argyle, 2002) to obtain information from the participants. The Prayer Type Scale is a four-point Likert scale with fifteen items. There are four subscales that measure distinct types of prayer such as colloquial prayer, petitionary prayer, ritual prayer, and contemplative prayer. The Oxford Happiness Questionnaire is a 29-item, 6-point Likert scale. The response alternatives for scale are: Strongly disagree (1), moderately disagree (2), slightly disagree (3), slightly agree (4), moderately agree (5), and strongly agree (6). The researcher pre-tested the instruments and they yielded a Cronbach alpha of .769 and .731 for Prayer Type Scale and Oxford Happiness Questionnaire scale respectively. This result confirmed that the two instruments were reliable.

The researcher obtained written permission from Tangaza University College, Trans-Nzoia County Education Office, National Commission for Science, Technology and Innovation (NACOSTI) and lastly local administrations of each school where the study took place.  Thereafter, the researcher proceeded to the research sites to collect data. The respondents consented to take part in the study by filling the informed consent forms. The questionnaires were assigned codes to ensure anonymity and confidentiality of the respondents. Finally, the questionnaires which consisted of the following sections, that is, demographic characteristics, prayer type questionnaire and subjective well-being questionnaire were administered to the participants. The field data was analysed using the SPSS version 23. Descriptive statistics were used to analyse data on participants’ demographic details such as age, gender, religious background, type of school and class level. To determine demographic differences among respondents’ subjective well-being, inferential statistics, that is, T-tests and One-Way ANOVA were conducted.

RESULTS

The main objective of this study was to examine socio-demographic differences in subjective well-being among secondary school students from faith-based schools in Kiminini Sub-County, Kenya. Prior to presentation of the findings, socio-demographic data of participants such as age, gender, religious background, type of school and class level were reported.

Demographic Characteristics of Respondents

Descriptive statistics were carried out and the socio-demographic details of the participants such as age, gender, religious background, type of school and class level are stated in Table 1.

Table 1: Demographic Characteristics of Participants  

Table 1 indicate that students within the age of 17 years old were the majority (N=143, 35.7%) followed by those who had 16 years old (N=139, 34.7%). Relatedly, students who were 15 years old had a representation of 14.0% (N=56) while those who had 19, 14 and 21 years old had the following representation. That is, 3%(N=12), 2.2%(N=9) and 0.7%(N=3) respectively. Lastly, students who had the age of 20 (N=2, 0.5%) and 13 (N=1, 0.2%) years old were the minority. Female respondents were the majority at 65.6 % (n = 263) while male respondents were 35.4% (n = 138).

Respondents from other Christian churches had a higher representation of 49.9% (n = 200) followed by the Catholics at 34.4% (n = 138). On the other hand, respondents with no religious affiliation and the Muslims were the least with 1.0% each (n = 4). Respondents from girls’ day and boarding schools were the majority at 49.6% (n = 199) and mixed day and boarding schools produced few participants at 2.7% (n = 11). Respondents in form 3 had the high representation in the study at 38.4% (n = 154). Form fours had the least representation of 29.4% (n = 118).

Socio-Demographic Differences and Subjective Well-Being of the Respondents.

Inferential statistics were carried out to investigate demographic differences in subjective well-being among secondary school students from faith-based secondary schools in Kiminini Sub-County, Kenya. Results are presented in the following sub-sections.

Age Differences and Subjective Well-Being of Respondents.

The study sought to understand subjective well-being of the participants across different ages. Participants were asked to indicate their ages in the questionnaires. One-Way ANOVA statistics were carried out and the findings are reported in Table 2.

Table 2: Age Differences and Subjective Well-Being

The results in Table 2 point out that age did not significantly determine respondents’ Subjective well-being; F(5, 396)=.840, p=.568. This indicates that age of the participants did not significantly influence their subjective well-being. Thus, participants within different age categories were likely to experience same levels of subjective well-being.

Gender Differences and Subjective Well-Being

This study aimed to establish the comparison between gender and subjective well-being of the respondents. In order to attain this aim, an independent sample t-test was carried out. Table 3 presents the findings.

Table 3: Gender Differences and Subjective Well-Being of Respondents 

Table 3 shows the results of the comparison between gender and subjective well-being of the respondents. The findings showed a significant difference between respondents’ gender and subjective well-being, t (399) =5.217, p=.000. This signifies that, males and females differ in terms of happiness, affect and satisfaction with life. These differences are likely to affect their subjective well-being.

Religious Background Differences and Subjective Well-Being

The study also strove to understand students’ subjective well-being with regard to different religious backgrounds. As a results participants were asked to indicate their religious affiliations. One-Way ANOVA was conducted and the results are presented in Table 4.

Table 4: Religious Background Differences and Subjective Well-Being  

The result of analysis of variances illustrates that there was an insignificant difference between religious background and SWB, F (6, 395) = .520, p = .761. According to the findings, religious background did not determine students’ well-being in faith-based secondary schools in Kiminini Sub-County, Kenya.

Type of School and Subjective Well-Being

The study sought to establish the comparison between the types of schools that participants frequented with regard to their subject well-being. To attain this aim, One-Way ANOVA statistics were conducted. The results are reported in Table 5. 

Table 5: Type of School and Subjective Well-Being

Table 5 suggests that there was a significant relationship between type of school and subjective wellbeing, ANOVA was significant F(2, 399) = 22.687, p = .000. This means that the levels of subjective well-being differed among the respondents due to the schools they frequented. Therefore, to identify where the difference occurred within the groups of schools, the researcher performed the post hoc test for differences as indicated in Table 7.

Descriptive Statistics for Type of School and Subjective Well-Being

Prior to Post Hoc test, the study intended to understand the descriptive statistics of type of school and subjective well-being among the respondents. This aided in comprehending well the mean differences with regard to subjective well-being. Thus, means of the two variables were computed, compared and the results are presented in Table 6.

Table 6: Descriptive Statistics of Type of Schools and Subjective Well-Being

Table 6 indicates that students from Boys’ Day and Boarding Schools experienced high levels of subjective well-being (M=133.444, SD=13.94722) as compared to those in Girls’ Day and Boarding Schools (M=117.4372, SD=17.51435), Mixed day and Boarding Schools (M=115.0000, SD=20.10970) and lastly those in Mixed Day Schools (115.0545, SD=16.21350). However, mean differences of subjective well-being among the respondents could not indicate clearly the significance differences. Therefore, as indicated in previous section, a post hoc test was conducted to test for significance of the mean differences. The results are reported in Table 7.

Table 7: Post Hoc Tests

*. The mean difference is significant at the 0.05 level.

The ANOVA test performed in Table 5 showed that there was a significant relationship between the type of school and SWB of respondents (F = 22.687, p = .000). Nevertheless, the results of post hoc test showed that there was a significant difference in the subject well-being of students who were in Boys’ Day and Boarding Schools with that of students who were in Girls’ Day and Boarding Schools (M=117.4372, p=.000), Mixed Day and Boarding Schools (M=115.0000, p=.003) and Mixed Day Schools (M=115.0545, p=.000). Similarly, findings indicated that there was a significant difference in subjective well-being of students who were in Girls’ Day and Boarding Schools with that of students in Boys’ Day and Boarding schools (M=133.4444, p=.000). These results suggests that Boys’ Day/Boarding Schools and Girls’ Day/Boarding Schools created conducive environment where students experienced happiness, positive affect and satisfaction with life.

Class Level and Subjective Well-Being.

The study intended to comprehend students’ subjective well-being in various class level. Thus, One-way ANOVA statistics were conducted and the findings are presented in Table 7.

Table 8: Class Level and Subjective Well-Being.

Table 7 presents the results of the ANOVA test which indicated that there is no significant differences between class levels and subjective well-being of respondents; F (3, 398) = 2.886, p = .057). This means that all the students were likely to encounter same levels of subjective well-being despite their class levels.

DISCUSSION

Relationship Between Demographic Characteristics and Subjective Well-Being

The study examined socio-demographic differences in subjective well-being among secondary school students from faith-based schools in Kiminini Sub-County, Kenya. In relation to age, the study found out that there was no a significant difference between age and subjective well-being of the participants F(5, 396)=.840, p=.568. This implies that age had no statistically significant influence of students’ subjective well-being. These findings could be due to general support offered to students while in school by significant others, thus enhancing their subjective well-being.

These findings disagreed with findings of Osamika and Ojasanya (2019) who established that ages of civil servants in Ibadan State, Nigeria determined their levels of life satisfaction (r=.07, p = 0.030). As per Osamika’s and Ojasanya’s findings, civil servants that had advanced in age were likely to encounter high levels of life satisfaction as compared to the younger ones.

The differences among the study of Osamika and Ojasanya with respect to the current study may be due to age differences. The study conducted by Anand (2016) is useful in understanding these differences. Anand established that life satisfaction often tends to be low at the age of 16, which signifies that the transition period to adulthood is generally challenging and uncertain among many young people. As opposed to Osamika and Ojasanya who included civil servants from Ibadan State, Nigeria, the current study only considered adolescents from faith-based schools in Kiminini Sub-County, Kenya. This explains the differences in terms of significance of relationship among the two variables.

With regard to gender, findings suggested that there a significant difference between respondents’ gender and subjective well-being, t (399) =5.217, p=.000. This means that gender of the participants played a role on their levels of subjective well-being. The findings of Thuku, (2016) concur with the results of the present study. The research showed that retired women from Nyeri County, Kenya experienced high levels of quality and satisfying life than men. Similarly, this study agrees with Santos’ et al. (2012) study which found gender differences in life satisfaction of t = 1.107, p<.05 among male (N = 469, M = 25.38, SD = 5.37) and female students (N = 500, M = 23.85, SD = 5.09) from Filipino University. In general, the common assumption among the above studies is that subjective well-being of individuals can be significantly determined by their gender.

With reference to religious background of the respondents, the study revealed that there was no significant difference between religious background and subjective well-being of respondents, F (6, 395) = .520, p = .761. This means that religious background of respondents had no effect on their levels of subjective well-being. It therefore follows that whether the participants were from Muslims, Christians or even from any other religious affiliations, all were likely to experience same levels of subjective well-being.

In relation to type of school of respondents, results showed that there was a significant difference with regard to subjective well-being of students in faith-based secondary schools in Kiminini Sub-County, Kenya F(2, 399) = 22.687, p = .000. The significant difference was due to subjective well-being of students who were in Girls’ Day and Boarding Schools with that of students in Boys’ Day and Boarding Schools (M=133.4444, p=.000). Similarly, there was also a significant difference in the subjective well-being of students who were in Boys’ Day and Boarding Schools with that of students who were in Girls’ Day and Boarding Schools (M=117.4372, p=.000), Mixed Day and Boarding Schools (M=115.0000, p=.000) and Mixed Day Schools (M=115.0545, p=.000).

The results of the current study were in agreement with those of Eryilmaz (2015) which was carried out among 421 adolescent Muslim students from Turkish high schools. The study revealed that students scored high in the following religious practices while in school: that is, praying (f = 23, % = 38.33), performing religious duties (f =19, % = 31.67), fasting (f = 6, % = 10), reading prayers five times per day (f   = 5, % = 8.34) and visiting the mosque on Fridays (f = 3, % = 5). The qualitative results also revealed that learners had better subjective well-being especially when engaged in religious practices, ranging from individual prayers to going to mosque on Fridays and other Holy days within the school premises.

These findings suggest that students from boys’ day and boarding schools as well as those from girls’ day and boarding schools encountered high levels of subjective well-being as compared to the rest of the participants from other type of schools. It can be argued that the above mentioned types of schools contributed to students’ levels of subjective well-being possibly through creating conducive environment for learning, healthy communications, emphasizing on religious/spiritual practices or positive relationship with colleagues and teachers.

With regard to class level of participants, findings indicated that there were no significant differences between class level and subjective well-being of respondents, F (3, 398) = 2.886, p = .057). This means that respondents class levels did not determine their levels of subjective well-being. In fact, all of them are likely to attain happiness, contentment and life satisfaction regardless of their class levels.  However, the findings of this study contradicted those of Santos et al. (2012) who discovered that education level led to differences in terms of subjective well-being of Filipino University students, whereby the fourth-year students scored high on life satisfaction than the rest of the students.

The differences between the findings of Santos et al. (2012) and the findings of the current study could be due to developmental concerns. For instance, the needs of adolescents’ mostly dwell around identity formation. However, this may not be the case with university students of which most of them are in early adulthood stage. They are likely to experience issues related to intimacy and isolation. Thus, this explains well why the two studies differ from each other.

CONCLUSION

The study established that there were no statistically significant differences between age, class level and religious background with regard to students’ subjective well-being. On the other hand, Gender and type of school caused significant differences on students’ level of subjective well-being. The study therefore concluded that type of school and gender determined students’ level of subjective well-being while age, religious background and class level did not have statistically significant influence on students’ subjective well-being.

RECOMMENDATIONS

Findings revealed that gender had a significant influence on students’ subjective well-being. Thus, counsellors especially those working in secondary schools may use this information to psycho-educate clients on negative issues that are likely to emerge due to gender differences. The findings of the study also revealed significant differences between type of school and learners’ subjective well-being. Thus, this study recommends that teachers, administrators and stakeholders in education sector should ensure that schools are conducive places that facilitates students’ general well-being.

REFERENCES

1. Agrawal, J., Murthy, P., Philip, M., Mehrotra, S., Thennarasu, K., John, J. P., Girish, N., Thippeswamy, V., & Isaac, M. (2011). Socio-demographic Correlates of Subjective Well-being in Urban India. Social Indicators Research, 101(3), 419–434. https:// doi.org/ 10.1007/ s11205-010-9669-5
2. Anand, P. (2016). Happiness , well-being and human development : the case for subjective measures (No. 83551).
3. Calys-Tagoe, B. N. L., Hewlett, S. A., Dako-Gyeke, P., Yawson, A. E., Baddoo, N. A., Seneadza, N. A. H., Mensah, G., Minicuci, N., Naidoo, N., Chatterji, S., Kowal, P., & Biritwum, R. B. (2014). Predictors of subjective well-being among older Ghanaians. Ghana Medical Journal, 48(4), 178–184. https://doi.org/10.4314/gmj.v48i4.2
4. Dang, T. N. H., & Sukontamarn, P. (2020). Education and Subjective Well-Being among Older Vietnamese: Exploring Gender Differences. Journal of Population and Social Studies, 28(1), 22–37. https://doi.org/10.25133/jpssv28n1.002
5. Das, K. V., Jones-Harrell, C., Fan, Y., Ramaswami, A., Orlove, B., & Nisha, B. (2020). Understanding subjective well-being: perspectives from psychology and public health. Public Health Reviews, 41(25), 1–32. https://doi.org/10.1186/s40985-020-00142-5
6. Diener, E., E., L. R., & Oishi, S. (2002). Sujective well-being: The science of happiness and life satisfaction. In C. L. R. (Ed.), Handbook of positive psychology (pp. 463–73). Oxford University Press.
7. Diener, E., Eunkook, M. S., Richard, E. L., & Heidi, L. S. (1999). Subjective Well-Being: Three Decades of Progress. Pyschological Bulletin, 125(2), 276–302.
8. Eryilmaz, A. (2015). Investigation of the Relations between Religious Activities and Subjective Well-being of High School Students. Educational Sciences: Theory & Practice, 15(2), 433–444. https://doi.org/10.12738/estp.2015.2.2327
9. Hills, P., & Argyle, M. (2002). The Oxford Happiness Questionnaire: a compact scale for the measurement of psychological well‐being. Personality and Individual Differences, 33, 1073–1082.
10. Juliane, W., & Mudi, B. I. (2018). Education Opportunity Effects on Subjective Well-Being among Female Employees in the County Government of Kakamega, Kenya. International Journal of Advanced Research and Review, 3(12), 27–33. www.ijarr.in
11. Osamika, B., & Ojasanya, T. (2019). Influence of socio-demographic factors , perceived happiness and psychological distress on life satisfaction among civil servants in Ibadan metropolis , Nigeria. Nigerian Journal of Psychological Research, 15, 23–28.
12. Poloma, M. M., & Pendleton, B. F. (1989). Prayer Types Scale in Christian, Jewish, and Muslim praying adults: One scale or a family of scales? Psychology of Religion and Spirituality, 7(3), 205–216. https://doi.org/10.1037/rel0000018.
13. Rishworth, A., Elliott, S. J., & Kangmennaang, J. (2020). Getting Old Well in Sub Saharan Africa: Exploring the Social and Structural Drivers of Subjective Wellbeing among Elderly Men and Women in Uganda. International Journal of Environmental Research and Public Health, 17(7). https://doi.org/10.3390/ijerph17072347
14. Rodrigues, L. R., Nader, I. D., Silva, A. T. M., Tavares, D. M. dos S., Assunção, L. M., & Molina, N. P. F. M. (2017). Spirituality and religiosity related to socio-demographic data of the elderly population. Revista Da Rede de Enfermagem Do Nordeste, 18(4), 429–436. https://doi.org/10.15253/2175-6783.2017000400002
15. Santos, M. C. J., Magramo, J., Oguan Jr., F., Paat, J. N. J., & Barnachea, E. A. (2012). Meaning in Life and Subjective Well – Being: Is a Satisfying Life Meaningful? Researchers World: Journal of Arts, Science & Commerce, 4(1), 32–40.
16. Shiah, Y. J., Chang, F., Chiang, S. K., & Tam, W. C. C. (2016). Religion and Subjective Well-Being: Western and Eastern Religious Groups Achieved Subjective Well-Being in Different Ways. Journal of Religion and Health, 55(4), 1263–1269. https://doi.org/10.1007/s10943-014-9905-4
17. Thuku, P. W. (2016). Influence of Socio-demographic Factors on Quality of Life of Retirees in Kenya. Imperial Journal of Interdisciplinary Research, 2(2), 90–100.
18. Upasna, J. (2010). Subjective Well-Being by Gender. Journal of Economics and Behavioural Studies, 1(1), 20–26.
19. Yamane, T. (1967). Statistics, An Introductory Analysis (2nd Editio). Harper and Row.
20. Yue, X. D., Hiranandani, N. A., Jiang, F., Hou, Z., & Chen, X. (2017). Unpacking the Gender Differences on Mental Health: The Effects of Optimism and Gratitude. Psychological Reports, 0(0), 1–11. https://doi.org/10.1177/0033294117701136