Understanding Cross-Classified Multilevel Models (CCMM)
Cross-Classified Multilevel Models (CCMM) represent an advanced statistical approach that extends beyond traditional hierarchical models to accommodate non-nested data structures. These models are particularly beneficial in health research, where data often involve complex relationships that do not fit neatly into hierarchical structures. CCMM allow researchers to account for multiple, overlapping contextual influences on health outcomes, such as the simultaneous impact of schools and neighborhoods on children's health.
Key Findings from the Systematic Review
The systematic review of empirical studies utilizing CCMM in health research reveals several critical insights:
- Increasing Adoption: There has been a steady increase in the use of CCMM since its inception, with a significant rise in publications since 2014.
- Diverse Applications: CCMM have been applied across various health domains, including body weight, mental health, and substance use, demonstrating the versatility of this method.
- Five Main Rationales: Researchers primarily use CCMM to examine concurrent contextual effects, account for non-independence in data, explore effects over time, conduct age-period-cohort analyses, and apply innovative modeling techniques.
Recommendations for Practitioners
For practitioners seeking to enhance their research through CCMM, the following recommendations are proposed:
- Clear Rationale: Clearly articulate the rationale for using CCMM, whether it is to address clustering in data or to explore contextual effects substantively.
- Comprehensive Reporting: Provide detailed information on sample sizes at all levels and report variance estimates and other relevant statistics to facilitate a comprehensive understanding of the data structure.
- Engage with Methodological Debates: Particularly for age-period-cohort models, engage with the ongoing methodological debates to ensure robust and unbiased analyses.
Encouraging Further Research
Despite the growing popularity of CCMM, there remains significant potential for further exploration and application in health research. Areas such as treatment adherence and dietary behaviors are underrepresented and could benefit from the nuanced insights that CCMM provide. Researchers are encouraged to delve into these areas, utilizing the flexibility of CCMM to uncover complex causal relationships in health outcomes.
For those interested in a deeper dive into the original research, please follow this link: Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices.
