A Monte Carlo evaluation of masked visual analysis in response-guided versus fixed-criteria multiple-baseline designs

We developed masked visual analysis (MVA) as a structured complement to traditional visual analysis. The purpose of the present investigation was to compare the effects of computer-simulated MVA of a four-case multiple-baseline (MB) design in which the phase lengths are determined by an ongoing visual analysis (i.e., response-guided) versus those in which the phase lengths are established a priori (i.e., fixed criteria). We observed an acceptably low probability (less than .05) of false detection of treatment effects. The probability of correctly detecting a true effect frequently exceeded .80 and was higher when: (a) the masked visual analyst extended phases based on an ongoing visual analysis, (b) the effects were larger, (c) the effects were more immediate and abrupt, and (d) the effects of random and extraneous error factors were simpler. Our findings indicate that MVA is a valuable combined methodological and data-analysis tool for single-case intervention researchers.