Related Experiment Video
Updated: Mar 1, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
Published on: April 11, 2025
Masked Visual Analysis: Minimizing Type I Error in Visually Guided Single-Case Design for Communication Disorders
Tara McAllister Byun1, Elaine R Hitchcock2, John Ferron3
1NYU Steinhardt School of Culture, Education, & Human Development.
Purpose:
Single-case experimental designs are widely used to study interventions for communication disorders. Traditionally, single-case experiments follow a response-guided approach, where design decisions during the study are based on participants' observed patterns of behavior. However, this approach has been criticized for its high rate of Type I error. In masked visual analysis (MVA), response-guided decisions are made by a researcher who is blinded to participants' identities and treatment assignments. MVA also makes it possible to conduct a hypothesis test assessing the significance of treatment effects.
Method:
This tutorial describes the principles of MVA, including both how experiments can be set up and how results can be used for hypothesis testing. We then report a case study showing how MVA was deployed in a multiple-baseline across-subjects study investigating treatment for residual errors affecting rhotics. Strengths and weaknesses of MVA are discussed.
Conclusions:
Given their important role in the evidence base that informs clinical decision making, it is critical for single-case experimental studies to be conducted in a way that allows researchers to draw valid inferences. As a method that can increase the rigor of single-case studies while preserving the benefits of a response-guided approach, MVA warrants expanded attention from researchers in communication disorders.
Related Concept Videos
Blinding
Group Design
Blind Procedures
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

