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International Conference on Applied Multilevel Modeling and Hierarchical Data Analysis - (ICAMMHDA-27)

11th - 12th June 2027 , Florence - Italy

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Call For Papers

The (ICAMMHDA-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Statistics, Data Science are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Multilevel modeling in educational research
  • Hierarchical data analysis techniques
  • Bayesian multilevel models applications
  • Statistical methods for longitudinal data
  • Multilevel modeling of health outcomes
  • Cross-classified models in social sciences
  • Random effects models for clustered data
  • Statistical approaches to nested data structures
  • Applications of multilevel modeling in psychology
  • Multilevel modeling in environmental studies
  • Statistical software for hierarchical modeling
  • Challenges in multilevel data analysis
  • Modeling variance components in multilevel data
  • Multilevel modeling for policy evaluation
  • Statistical techniques for meta-analysis
  • Multilevel structural equation modeling
  • Applications in public health research
  • Statistical inference in multilevel frameworks
  • Comparative studies using multilevel methods
  • Future directions in hierarchical modeling

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.