Augmenting analysis of single-case math interventions with Bayesian multilevel models: Examining effect visualization...

Area of Science:

• Educational Psychology
• Quantitative Psychology
• Intervention Research

Background:

• Math interventions show varied impacts, from rapid changes to gradual skill development.
• Analyzing these diverse outcomes requires methods that integrate visual and quantitative approaches.
• Single-case designs are valuable but can present challenges in analyzing nuanced intervention effects.

Purpose of the Study:

• To examine how Bayesian multilevel models can effectively integrate visual and quantitative analysis of single-case designs.
• To quantify and visualize uncertainty in the analysis of math intervention impacts.
• To demonstrate the augmentation of single-case design analysis without compromising technical sophistication or interpretive ease.

Main Methods:

• Utilized data from two separate math interventions involving secondary students.
• Applied Bayesian multilevel models to single-case design data.
• Integrated visual and quantitative analysis techniques.

Main Results:

• Bayesian models effectively augment the analysis of single-case designs.
• These models maintain the technical sophistication of quantitative analysis and the interpretive ease of visual analysis.
• The methods help quantify and visualize uncertainty in effect magnitudes, crucial for diverse intervention outcomes.

Conclusions:

• Bayesian multilevel models offer a powerful approach to analyzing single-case designs in math interventions.
• These models improve the understanding of intervention effects and associated uncertainty.
• Future research should explore further alignment of Bayesian modeling with visual analysis for single-case interventions.