Design and Analysis of Monte Carlo Experiments: Attacking the Conventional Wisdom

Area of Science:

• Statistics
• Computational Statistics
• Simulation Methods

Background:

• Monte Carlo experiments are crucial for validating statistical models, particularly structural equation modeling.
• Designing and analyzing these experiments presents challenges in statistical precision, external validity, and computational resources.
• Current conventional wisdom for Monte Carlo studies may not be optimal for modern computational demands.

Purpose of the Study:

• To critically examine conventional approaches to designing and analyzing Monte Carlo experiments.
• To propose alternative, more efficient methodologies for conducting Monte Carlo studies.
• To address the challenges of statistical precision, external validity, and resource constraints in Monte Carlo experimentation.

Main Methods:

• The study critically evaluates traditional methods for Monte Carlo experiment design and analysis.
• It suggests specifying explicit meta-models to link performance statistics with experimental conditions.
• Recommendations include using incomplete experimental designs and common random numbers for efficiency and precision.

Main Results:

• Conventional Monte Carlo design often relies on full factorial designs and excessive replications, potentially limiting external validity.
• Explicit meta-models can improve the analysis of simulation output.
• Incomplete designs and reduced replications can enhance external validity and save computational resources.

Conclusions:

• A shift from conventional Monte Carlo experiment design is recommended for improved efficiency and generalizability.
• Specifying meta-models and employing incomplete designs are key strategies.
• Balancing statistical precision with external validity is achievable through thoughtful experimental design and resource management.