Visual aids and structured criteria for improving visual inspection and interpretation of single-case designs

Because behavior analysis is a data-driven process, a critical skill for behavior analysts is accurate visual inspection and interpretation of single-case data. Study 1 was a basic study in which we increased the accuracy of visual inspection methods for A-B designs through two refinements of the split-middle (SM) method called the dual-criteria (DC) and conservative dual-criteria (CDC) methods. The accuracy of these visual inspection methods was compared with one another and with two statistical methods (Allison & Gorman, 1993; Gottman, 1981) using a computer-simulated Monte Carlo study. Results indicated that the DC and CDC methods controlled Type I error rates much better than the SM method and had considerably higher power (to detect real treatment effects) than the two statistical methods. In Study 2, brief verbal and written instructions with modeling were used to train 5 staff members to use the DC method, and in Study 3, these training methods were incorporated into a slide presentation and were used to rapidly (i.e., 15 min) train a large group of individuals (N = 87). Interpretation accuracy increased from a baseline mean of 55% to a treatment mean of 94% in Study 2 and from a baseline mean of 71% to a treatment mean of 95% in Study 3. Thus, Study 1 answered basic questions about the accuracy of several methods of interpreting A-B designs; Study 2 showed how that information could be used to increase the accuracy of human visual inspectors; and Study 3 showed how the training procedures from Study 2 could be modified into a format that would facilitate rapid training of large groups of individuals to interpret single-case designs.     

TOWARD THE DEVELOPMENT OF STRUCTURED CRITERIA FOR INTERPRETATION OF FUNCTIONAL ANALYSIS DATA

Using functional analysis results to prescribe treatments is the preferred method for developing behavioral interventions. Little is known, however, about the reliability and validity of visual inspection for the interpretation of functional analysis data. The purpose of this investigation was to develop a set of structured criteria for visual inspection of multielement functional analyses that, when applied correctly, would increase interrater agreement and agreement with interpretations reached by expert consensus. In Study 1, 3 predoctoral interns interpreted functional analysis graphs, and interrater agreement was low (M = .46). In Study 2, 64 functional analysis graphs were interpreted by a panel of experts, and then a set of structured criteria were developed that yielded interpretive results similar to those of the panel (exact agreement = .94). In Study 3, the 3 predoctoral interns from Study 1 were trained to use the structured criteria, and the mean interrater agreement coefficient increased to .81. The results suggest that (a) the interpretation of functional analysis data may be less reliable than is generally assumed, (b) decision-making rules used by experts in the interpretation of functional analysis data can be operationalized, and (c) individuals can be trained to apply these rules accurately to increase interrater agreement. Potential uses of the criteria are discussed.