A novel modeling framework for ordinal data defined by collapsed counts.

Area of Science:

• Public Health
• Biostatistics
• Quantitative Psychology

Background:

• Adolescent alcohol use presents a significant public health challenge.
• Current statistical methods for analyzing adolescent drinking data are limited, often employing linear or restrictive categorical models that fail to capture the underlying count process.
• This disconnect between theoretical constructs and analytical models hinders accurate research.

Purpose of the Study:

• To introduce a novel ordinal modeling framework that explicitly connects ordinal responses to an underlying count distribution.
• To provide researchers with a method to analyze count data, even when only ordinal outcomes are observed.
• To enhance the statistical analysis of adolescent alcohol use and similar count-related public health issues.

Main Methods:

• Development of ordinal negative binomial and ordinal zero-inflated negative binomial models.
• Utilizing maximum likelihood estimation to fit count models to ordinal data.
• Validation through simulation studies and application to real-world data from the 2010 National Survey of Drug Use and Health.

Main Results:

• The proposed ordinal modeling approach effectively links ordinal responses to underlying count distributions.
• Simulation studies confirmed the utility of the ordinal negative binomial and ordinal zero-inflated negative binomial models.
• Empirical data analysis demonstrated the advantages of the new framework over existing statistical methods.

Conclusions:

• The novel ordinal modeling framework offers a statistically robust and theoretically sound approach for analyzing count data presented as ordinal outcomes.
• This methodology addresses the limitations of current models, providing a more accurate representation of adolescent alcohol use patterns.
• The enhanced analytical capabilities can lead to better-informed public health interventions and strategies.