visual-analysis

Visual analysis for single-subject ABA data applies a criteria-based framework evaluating within-condition features like level, trend, and variability, alongside between-condition comparisons of immediacy, overlap, and consistency to determine treatment effects.

Within-condition analysis in single-subject designs evaluates level, trend, and variability inside a single phase, while between-condition analysis compares immediacy, overlap, and replication across different phases to infer clinically meaningful behavior change.

To evaluate treatment effects during a withdrawal phase, apply structured visual analysis to graphed single-subject data, comparing immediacy and overlap across baseline, intervention, and replication phases to confirm whether behavior change is consistent.

Yes, structured visual analysis works for DTT programs and FA-based interventions, applying criteria-based within-condition and between-condition assessments to single-subject graphs to guide clinical decision-making regarding phase changes.

When interpreting single-subject graphed data, look for within-condition features of level, trend, and variability, and evaluate between-condition immediacy, overlap, and consistency to ensure rigorous and consistent interpretation of treatment effects.