Masked Visual Analysis: Minimizing Type I Error in Visually Guided Single-Case Design for Communication Disorders

Abstract

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.

Figures

Figure 1.

Schematic example of multiple-baseline across-subjects data. The dotted line represents the point of introduction of biofeedback treatment. Adapted from “Investigating the use of traditional and spectral biofeedback approaches to intervention for /r/ misarticulation,” by T. McAllister Byun and E. Hitchcock, 2012, American Journal of Speech-Language Pathology, 21, pp. 207–221. Copyright © 2012 by American Speech-Language-Hearing Association. Adapted with permission.

Figure 2.

A rising or unstable baseline can complicate the interpretation of single-case data. Schematic example adapted from “Investigating the use of traditional and spectral biofeedback approaches to intervention for /r/ misarticulation,” by T. McAllister Byun and E. Hitchcock, 2012, American Journal of Speech-Language Pathology, 21, pp. 207–221. Copyright © 2012 by American Speech-Language-Hearing Association. Adapted with permission.

Figure 3.

Schematic data illustrating decision points in a masked visual analysis design. y axis = percent of syllable-level tokens rated correct by blinded listeners. Vertical dotted lines represent boundaries between phases of the study. Horizontal dotted lines represent baseline mean for each participant. DP = data point. a: Example of establishing a stable baseline (data from Cohort 1). b: Example of identifying a response to treatment (data from Cohort 2).

Figure 4.

Schematic data illustrating the process of guessing overall treatment order (data from Cohort 2). The y-axis represents percent of syllable-level tokens rated correct by blinded listeners. Vertical dotted lines represent boundaries between phases of the study. Horizontal dotted line represents baseline mean for each participant. DP = data point.