Optimizing student mental health monitoring in the Netherlands: a factor analytic approach

Background

The “Monitor on Mental Health and Substance Use among Students in Higher Education” (MMMS) is a vital tool for tracking student well-being in the Netherlands. However, extensive survey length often leads to high respondent burden and declining data quality. This study aimed to streamline the MMMS mental health component, which comprises several validated short-form questionnaires, by removing redundant instruments in their entirety to maintain the diagnostic integrity of the remaining scales.

Methods

Data from two Dutch student cohorts were utilized (2023: N = 32,558; 2025: N = 27,446). A three-step workflow was applied. First, Exploratory Factor Analysis on 2023 data identified latent factors and redundant scales to shorten the questionnaire. Second, Confirmatory Factor Analysis on 2025 data tested the model fit of this abbreviated structure. Third, reliability and measurement invariance were assessed across gender and age groups to ensure consistent performance.

Results

Analyses identified three distinct factors: psychological well-being, mental strain, and resilience and functioning. Removing redundant scales reduced the mental health component by 55%. The shortened model demonstrated improved construct validity (Average Variance Extracted = 43.0% to 49.8%) and substantial gains in fit (CFI = .810 to .885; TLI = .799 to .875), with acceptable absolute fit (RMSEA = .069; SRMR = .057). Internal consistency was robust (omega = .84 to .92). Metric invariance was confirmed across demographics, indicating a stable factor structure, though scalar invariance was only partially supported.

Conclusions

The MMMS mental health section can be significantly condensed without compromising structural integrity. By strategically removing redundant instruments, we reduce participant fatigue, thereby securing the long-term viability and quality of national student well-being surveillance.

Key messages

• Strategic removal of redundant instruments can reduce the length of specific monitor components by over 50% while maintaining or improving construct validity.

• The abbreviated mental health component of the MMMS provides a robust framework for monitoring student well-being across diverse demographic groups.