PSMHSN Dataset

Published: 29 April 2024| Version 1 | DOI: 10.17632/8p93g3c2tj.1


Objective: The purpose of this study was to implement and analyze the psychometric properties of a newly developed mental health instrumentation tool within the general population residing in Kathmandu, Nepal. Methods: This study was a quantitative pilot research study to gather data on the psychometric qualities of a new assessment tool for adults in Nepal. The implementation of Perceived Stigma-Based Mental Health Scale (PSMHSN) was required to be completed with an inclusion of other standardized tools: Beck’s Depression Inventory (BDI), Generalized Anxiety Disorder (GAD-7), and Perceived Stress Scale (PSS-4). These instrumentation tools were translated into Nepali language to increase language receptivity and implemented over a 4-week period. Pearson’s correlation, Cronbach's alpha, inter-item correlation, exploratory factor analysis (EFA) and Bayesian Factor Analysis were utilized to analyze the data. Results: A total of fifty-seven adults of various ages completed the instrumentation tools. The EFA, Scree plot, and Eigenvalue analysis produced two construct factors. The model fit indices of the PSMHSN questions revealed that superior model fit was achieved through the elimination of questions 1, 3, 4, 5, 10 and 11. Bootstrapped Pearson’s correlation presented adequate correlation (r = .51, CI95% [.224, .712]) and extreme Bayes factor evidence (BF10 = 1040.98) for convergent validity between the PSMHSN and the GAD, and moderate Bayes factor evidence for divergent validity between the PSMHSN and BDI (r = .31, BF10 = 4.54), and the PSMHSN and the PSS (r = .29, BF10 = 3.50). The PSMHSN possessed excellent unidimensional reliability, with a posterior odds for good to excellent reliability at 99.7%. Conclusion: The integration of PSMHSN considering stigma and cultural context adaptability was essential in gaining a deeper understanding of the symptomatology of the mental health dimension anxiety.



Shenandoah University


Psychometrics, Exploratory Factor Analysis