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American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
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American Journal of Cancer Prevention
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American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
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Journal of Preventive Medicine
American Journal of Food and Nutrition
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A Study of Positive Mental Health on Bengali Speaking Students of Higher Education in India and Bangladesh

Subir Sen, Surajit Mahato, Gurudas Mandal, Birbal Saha*

Associate Professor, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India

Abstract: Positive Mental Health (PMH) is a measure of an individual’s present mental state. A study on PMH is done on higher education students of India and Bangladesh with verification of validity and reliability of the scale used to measure the PMH. Present study is done to verify validity and reliability of the PMH scale with respect to the respondents of India and Bangladesh. Further it is used to compare the PMH between men and women for India, Bangladesh and both the countries. The descriptive survey method is followed for the present study. Sample size for the present study is 617 and data is collected by simple random sampling technique from India and Bangladesh. PMH scale by is administered for the present study. Descriptive (Mean, SD, SE and Coefficient of Correlation) and inferential statistics (Cronbach’s α, McDoland’s ω, Guttman’s λ2, Confirmatory Factor Analysis (CFA), Principal Component Analysis (PCA), and Structure Equation Modelling (SEM)) are used by applying SPSS-26, JASP-0.18.1. PMH scale is valid and reliable for Bengali speaking higher education students of both the countries and it is reflected by the measures of correlation, validity, reliability, PCA, CFA and SEM. PMH differed significantly across female responders from India and Bangladesh. Differences in demographics, attitudes toward learning, and gender-related societal policies may explain the mental health similarities and disparities observed between respondents from India and Bangladesh. Further elements could play a role in the mental health challenges experienced by female students in Bangladesh, indicating the need for a comprehensive, multivariate analysis.

Keywords: PMH, Correlation, Validity, Reliability, PCA, CFA, SEM, India, Bangladesh

Received April 16, 2025; Revised May 17, 2025; Accepted May 25, 2025

Citation: Subir Sen, Surajit Mahato, Gurudas Mandal, Birbal Saha, A Study of Positive Mental Health on Bengali Speaking Students of Higher Education in India and Bangladesh. American Journal of Educational Research, 13 (5). 268-276. January 2025. doi:10.12691/education-13-5-1

1. Introduction

The volume of publications concerning PMH and well-being is currently surging at an exponential pace [2]. The mental health with two-continua model [3,4,5,6,7] showed that negative mental health (NMH) and PMH are interrelated although they are separate dimensions. Two major theories clarify the key elements of PMH and the idea of well-being. The eudaimonic perspective emphasizes human potential and optimal functioning, while the hedonic perspective concentrates on feelings such as happiness and life satisfaction. PMH is viewed as a representation of overall emotional, psychological, and social well-being since both approaches can be utilized [3,8]. Positive mental health signifies a comprehensive state of well-being where individuals can successfully handle life’s challenges, recognize their potential, work efficiently, and make valuable contributions to their communities. It includes emotional, psychological, and social aspects, promoting resilience, nurturing healthy relationships, and fostering a sense of purpose. Instead of merely evading mental disorders, positive mental health focuses on flourishing, allowing individuals to experience increased joy, fulfillment, and an enhanced overall quality of life.

2. Literature Review

Comprehensive questionnaires incorporate items pertaining psychopathology and PMH using GHQ by [9] or examine various dimensions of PMH using MHC-SF by [10]. Vaughan [11] examined “Mental Health Measurement in the Post-COVID-19 Era,” evaluating the DASS-21 (psychometric properties) among athletes and non-athletes. Their findings contribute to understanding mental health of athletes and their support together with comparative analyses with the non-athletes. Mahato and Das [12] conducted a study on mental well-being among students with respect to gender, institution and residence: insights from Purulia district, West Bengal. Main objectives of the study are to explore the positive mental well-being among the students. Das and Mahato [13] studied positive mental health using clustering technique.

Boufellous [14] studied the Positive Mental Health. The Spanish version's general population validity is the goal of this study. Together with CFA and convergent validity with both risk and protective variables, descriptive analyses were also conducted on the items. When applied in many psychological contexts, particularly in the healthcare of the Spanish populace, the PMH scale possesses the required validity and reliability.

The Positive Mental Health Scale (PMHS) was intended to be translated into Turkish by [15]. A total of 360 college students, whose ages ranged from 18 to 25, took part in the study. Following an analysis of the scale's linguistic equivalence, validity and reliability were determined. Internal consistency, exploratory, and confirmatory factor analyses were used to evaluate the scale’s psychometric qualities.

Almubaddel [16] constructed the Positive Mental Health (PMH) scale of Saudi Arabian version. The objective of the study was to validate the scale which was translated from English into Arabic for use in Saudi Arabia. The study involved 1148 adult volunteers from public universities in Saudi Arabia. The current study’s results showed that the uni-factorial model satisfactorily fits the data based on exploratory and confirmatory factor analyses in various subsamples.

The study of Hasan [17] set out to examine the psychometric qualities of the PMH-scale in Bangla and validate it using the Brunel Mood Scale (BRUMS) and the Brief Aggression Questionnaire (BAQ). The study’s sample comprised 298 members of Bangladesh's general population.

The purpose of study of Naghavi [18] was to validate the Positive Mental Health Scale (PMH-Scale) in Persian. The PMH-Scale’s validity and reliability in Persian were determined using a sample of 573 Iranian students. It was shown that PMH moderated the relationship between depressive symptoms and suicidal thoughts and actions. The findings imply that the PMH-Scale is a quick, accurate, and legitimate indicator of psychological and subjective well-being that may be applied to research settings and Iranian student populations.

Present work is a comparison between Bengali speaking higher education students from India and Bangladesh. This comparison is done in several aspects like correlation, validity, reliability, PCA, CFA, SEM and t-test.

3. Objectives

Following objectives are considered for the present study:

a.To find out ‘validity of the PMH scale’ with respect to the respondents of India, Bangladesh and both the countries.

b.To find out ‘reliability of the PMH scale’ with respect to the respondents of India, Bangladesh and both the countries.

c.To check different parameters for the PMH scale by applying PCA, CFA and SEM with respect to the respondents of India, Bangladesh and both the countries.

d.To compare PMH of respondents of India and Bangladesh.

4. Hypotheses

Following hypotheses are constructed to fulfill the objectives stated above:

H₀₁: ‘There is no significant difference between the students of India and Bangladesh with respect to PMH’.

H₀₂: ‘There is no significant difference between the male and female students of India with respect to PMH’.

H₀₃: ‘There is no significant difference between the male and female students of Bangladesh with respect to PMH’.

H₀₄: ‘There is no significant difference between the male students of India and Bangladesh with respect to PMH’.

H₀₅: ‘There is no significant difference between the female students of India and Bangladesh with respect to PMH’.

5. Methodology

Method: Descriptive survey research is applied to do the research work.

Sample and Sampling: Sample size for the present study is 617 (513 from India and 104 from Bangladesh). Subjects are selected by random sampling method.

Variables: Positive Mental Health and dichotomous variables like gender (male, female) and country (India, Bangladesh) are considered as variables.

Tool used: ‘Positive Mental Health Scale (PMH-Scale)’ developed by Lukat [1] is administered for the present study to measure Positive Mental Health.

Techniques followed: Statistical measures like descriptive statistics (Mean, SD and SE of means) and inferential statistics like t-test, coefficient of correlation, reliability analysis, CFA, PCA, and SEM have been done to analyze the collected data for the present work. Softwares SPSS-26 and JASP 0.18.1 are used to calculate statistical measures.

6. Results and Discussions

• Correlation

From table 1, table 2 and table 3, it is clear that Item-item and item-PMH correlations for respondents of India, Bangladesh and both the countries are significant at .001 level of significance. So, convergent validity under construct validity may be assumed because PMH is a unidimensional scale.

Table 1. Item-item and item-PMH correlations for Indian respondents [*** means p< .001]

• Reliability

Table 4 represents the ‘Cronbach’s α’, ‘McDonald's ω’ and ‘Guttman’s λ-2’ for respondents of India, Bangladesh and both the countries. Here, highest value of ω is 0.85 and least value of ω is 0.826. Similarly, highest value of α and λ-2 are 0.85 and 0.851 respectively; least value of α and λ-2 are 0.82 and 0.827 respectively. Considering all the cases it may be concluded that PMH scale is highly reliable.

Table 2. Item-item and item-PMH correlations for respondents of Bangladesh [** means p < .002]

From table 5, it may be concluded that for all the cases if any item is dropped the reliability measures (ω, α and λ-2) decreases. It reflects the essentiality of every item in the PMH scale.

Table 3. Item-item and item-PMH correlations for India and Bangladesh [*** means p < .001]

• Principal Component Analysis (PCA)

To ascertain whether the data are appropriate for factor analysis, the ‘Kaiser-Meyer-Olkin Test’ [19] is a statistical test used. ‘KMO’ assesses model’s the quality of each observed variable and overall sample adequacy. KMO is determined by the correlation between the variables used in the particular measuring tool. It is a scale ranging from 0 to 1, where values closer 1 designate a correlation between the variables and data that used to formulate it suitable for factor analysis; otherwise, the variables may be considered as uncorrelated and as a result it might not be influenced by a common factor. Here, overall measures are greater than 0.8 which indicate that the samplings are adequate.

Table 4. Reliability statistics for respondents of India, Bangladesh and both the countries

Table 5. Reliability statistics for respondents of India, Bangladesh and both the countries if items are dropped

Table 6. Kaiser-Meyer-Olkin Test for respondents of India, Bangladesh and both the countries

Table 7. Component Loadings for respondents of India, Bangladesh and both the countries

Comparable to correlation coefficients, component loadings can be squared to determine the amount of variation that is explained. Consequently, the component loadings indicate the extent to which a component accounts for the variance in a variable. Here, for all cases values are satisfactory.

• Confirmatory Factor Analysis (CFA)

The degree to which the variance of items (variables) contributes to the variation of the PMH is explained by R-squared. Every item contributed to the variation of PMH for India, Bangladesh and both the countries.

We take into account the ‘Comparative Fit Index (CFI)’ and ‘Tucker-Lewis Index (TLI)’ in the table of ‘Fit indices’. The TLI and CFI values are, respectively, 0.933 and 0.950 for Indian respondents. The fact, these values exceed 0.90 for India, Bangladesh, and both countries data. It suggests a suitable model fit.

Factor loadings for respondents of India, Bangladesh and both the countries are satisfactory and significant at .001 levels of significance. Standard errors are very low for all the cases.

• Structure Equation Modelling (SEM)

All the loadings for three cases are significant at .001 levels of significance. The differences are small for three cases. As a result factor loadings for respondents of India, Bangladesh and both the countries are similar in nature.

Path diagrams are directed graph that show the pattern of relationships between variables. The directions of the causal linkages between variables are indicated by straight arrows connecting them. Factor loadings are mentioned on the straight arrows. An outline for a structural equation model (SEM); For latent variables, use oval/circular forms are represented by c1, c1 and CT for India, Bangladesh and both the countries respectively. Rectangular forms are used to depict the measured variables. Measurements errors in each observed variable are shown by the curved arrows are low for three cases. Path diagrams for all the cases are satisfactory for the construction of PMH scale.

Table 8. R-Squared values for respondents of India, Bangladesh and both the countries

Table 9. Fit indices for respondents of India, Bangladesh and both the countries

Table 10. Factor loadings for respondents of India, Bangladesh and both the countries (CFA)

Table 11. Factor loadings for respondents of India, Bangladesh and both the countries (SEM)

Table 12. Path diagrams for respondents of India, Bangladesh and both the countries

• Comparison

√ India vs Bangladesh

Table 13. Group statistics for India and Bangladesh

Table 14. t-test between students of India and Bangladesh

To compare the state of PMH between the students of India and Bangladesh, let us recall the ‘null hypothesis’:

H₀₁: ‘There is no significant difference between the students of India and Bangladesh with respect to PMH’.

Results showed that the difference in means between the students of India and Bangladesh is significant at .01 level of significant (t=2.646, Sig. =.008 for df=615). As a result, the ‘null hypothesis’ H₀₁ is rejected. Hence, alternative hypothesis Ha1, which states ‘There is a significant difference between the students of India and Bangladesh with respect to PMH’ is accepted. From table 13, PMH of Indian students found better.

√ India (Male vs female)

Table 15. Group statistics for ‘Male and Female students’ of India

Table 16. t-test ‘between male and female students’ of India

To compare the state of PMH between the ‘male and female’ students of India, let us recall the null hypothesis:

H02: ‘There is no significant difference between the ‘male and female’ students of India with respect to PMH’.

Results showed that the difference in means between the ‘male and female’ students of India is not significant (t=.992, Sig. =.322 for df=511). As a result, the ‘null hypothesis’ H₀₂is accepted.

√ Bangladesh (Male vs female)

Table 17. Group statistics for Male and Female students of Bangladesh

Table 18. t-test ‘between male and female students’ of Bangladesh

To compare the state of PMH between the ‘male and female’ students of India, let us recall the null hypothesis:

H₀₃: ‘There is no significant difference between the ‘male and female’ students of Bangladesh with respect to PMH’.

Results showed that the difference in means ‘between the male and female students’ of India is significant (t=2.108, Sig. =.038 for df=102). As a result, the ‘null hypothesis’ H₀₃is not accepted.

Hence, alternative hypothesis Ha3, which states ‘There is a significant difference between the male and female students of Bangladesh with respect to PMH’ is accepted. From table 17, PMH of male students found better.

√ Male (India vs Bangladesh)

Table 19. Group statistics for India and Bangladesh male students

Table 20. t-test between male students of India and Bangladesh

To compare the state of PMH between the male students of India and Bangladesh, let us consider the null hypothesis:

H₀₄: ‘There is no significant difference between the male students of India and Bangladesh with respect to PMH’.

Results showed that the difference in means between the students of India and Bangladesh is significant at .01 level of significant (t=1.124, Sig. =.262 for df=266). As a result, the ‘null hypothesis’ H₀₄ is failed to be rejected.

√ Female (India vs Bangladesh)

Table 21. Group statistics for India and Bangladesh female students

Table 22. t-test between female students of India and Bangladesh

To compare the state of PMH between the female students of India and Bangladesh, let us consider the null hypothesis:

H₀₅: ‘There is no significant difference between the female students of India and Bangladesh with respect to PMH’.

Results showed that the difference in means between the students of India and Bangladesh is significant at .01 level of significant (t=2.983, Sig. =.003 for df=347). As a result the null hypothesis H₀₅ is rejected. Hence, alternative hypothesis Ha5, which states ‘There is a significant difference between the female students of India and Bangladesh with respect to PMH is accepted. From table 21, PMH of Indian female students found better.

Findings

Major findings are listed below:

1. Each item of the scale is significantly related to each other and total score of PMH.

2. McDonald's ω, Cronbach’s α and Guttman's λ-2 for respondents of India, Bangladesh and both the countries showed the high reliability of the measure of PMH.

3. Data are appropriate for factor analysis, overall measures of the Kaiser-Meyer-Olkin Test are greater than 0.8 which indicate that the samplings are adequate.

4. Component loadings in PCA are satisfactory.

5. In CFA, R-squared values of every item contributed to the variation of PMH for India, Bangladesh and both the countries are satisfactory.

6. Factor loadings in CFA for respondents of India, Bangladesh and both the countries are satisfactory and significant at .001 levels of significance.

7. In SEM, all the loadings for three cases (India, Bangladesh and both the countries) are significant at .001 levels of significance. The differences are small for three cases (India, Bangladesh and both the countries).

8. In SEM, from path diagram latent variables are low compared to factor loadings for India, Bangladesh and both the countries.

9. There is a significant difference between the students of India and Bangladesh with respect to PMH’ is accepted. From table 13, PMH of Indian students found better.

10. Difference in means between the ‘male and female’ students of India is not significant

11. There is ‘no significant difference between the male and female students’ of Bangladesh with respect to PMH

12. There is ‘no significant difference between the male students’ of India and Bangladesh with respect to PMH.

13. There is a ‘significant difference between the female students’ of India and Bangladesh with respect to PMH.

Conclusion

The item scores for the sets of respondents from Bangladesh, India, and both nations in this PMH study are shown noteworthy correlations. Results using PCA, CFA, and SEM are also comparable across all cases. The comparison of respondents from Bangladesh and India is reliable because reliability scores are also noteworthy. According to PMH scores, female students in Bangladeshi and Indian higher education differ significantly from one another. This discrepancy might result from societal cultural differences, attitudes toward female students' education, and a lack of learning opportunities. The demographic differences between these two countries, attitude towards learning, openness in societal policies regarding gender and biases may be other dimensions that may be the causes for the possible similarities and differences between the respondents of India and Bangladesh. There may be some other constructs responsible for the backwardness of women students of Bangladesh regarding PMH. An extensive and multivariate study may be considered to enlighten the present state of mental health.

References

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