A Meta-Visual-Analysis of Single-Case Experimental Design Research

Area of Science:

• Behavioral Science
• Research Methodology
• Psychology

Background:

• Visual analysis is standard for detecting functional relations in single-case experimental design (SCED) research.
• Existing synthesis methods may not fully capture the nuances of visual analysis in SCED.

Purpose of the Study:

• Introduce Meta-Visual-Analysis (MVA) as a novel approach to synthesize SCED data.
• Detail MVA's utility in reviewing, organizing, and directing future SCED research.
• Enhance the understanding of intervention effectiveness and generality across SCED studies.

Main Methods:

• MVA quantifies effect sizes based on changes in level, trend, variability, and trend-adjusted level.
• Percentage point differences are estimated for each participant across studies.
• Standardized MVA effect sizes are aggregated and graphically displayed, similar to individual SCED graphs.

Main Results:

• MVA provides a standardized, visual synthesis of SCED data.
• Aggregated MVA graphs allow for visual analysis of intervention effectiveness and generality.
• The approach facilitates a systematic comparison across multiple SCED studies.

Conclusions:

• MVA offers a structured method for synthesizing SCED research.
• This approach improves the review and organization of existing SCED literature.
• MVA can guide future research syntheses and identify patterns in intervention effects.