Development and Validation of Novel Mental Health Assessment Scales for Students (MASS) in India

Background: Student mental health is increasingly recognized as a multidimensional issue influenced by stress, psychopathology, risk factors, resilience, and functional outcomes. In India, a culturally grounded, comprehensive assessment tool tailored to this context has been lacking. Objective: This study aimed to develop and validate the Mental Health Assessment Scales for Students (MASS), a multi-domain, psychometric battery designed to assess mental health comprehensively among Indian university students. Methods: Six scales were developed through literature review, expert consultation, and culturally sensitive item generation: Stress (13 items), Psychiatric Symptoms (10), Mental Health Risk (21), Risk Factors (6), Positivity (24), and Functioning (22). A sample of 442 undergraduate students (aged 18 - 30) was recruited from K.J. Somaiya Institute of Technology, Mumbai. Data were collected via a self-administered digital tool, and psychometric evaluation involved Exploratory and Confirmatory Factor Analyses (EFA and CFA), as well as internal consistency assessment using Cronbach’s alpha. Results: The MASS battery demonstrated strong psychometric properties across its six subscales. Exploratory Factor Analysis (EFA) supported unidimensional structures for all scales except Functioning, which showed a two-factor structure encompassing academic and interpersonal functioning. Confirmatory Factor Analysis (CFA) indicated excellent model fit for all scales (CFI = 0.943 - 0.986, RMSEA = 0.043 - 0.065). Internal consistency was high for most scales, with Cronbach’s alpha values ranging from 0.815 to 0.973. The Risk Factors scale showed acceptable reliability (α = 0.667). Inter-scale correlation analysis revealed significant associations: Psychological Stress, Psychiatric Symptoms, and Mental Health Risk were positively correlated (r = 0.65 - 0.78), while Positivity showed strong negative correlations with symptom domains (r = −0.48 to −0.69). Functioning was moderately correlated with both symptom severity and resilience. K-means clustering enabled severity stratification into four categories: normal, mild, moderate, and severe, allowing for targeted triage. The MASS tool successfully differentiated students by psychological burden and functional impact, confirming its utility in early identification and intervention planning. Conclusion: The MASS is a reliable and valid tool for comprehensive mental health assessment among university students in India. Its multi-domain structure enables early detection, risk stratification, and facilitation of targeted interventions, while its digital format ensures scalability in both clinical and research settings.