An Alternative to PND

Single-case researchers traditionally have relied on visual analysis of graphs for judging intervention success. Despite the well-documented unreliability of visual judgments (Brossart, Parker, Olson, & Lakshmi, 2006; DeProspero & Cohen, 1979; Harbst, Ottenbacher, & Harris, 1991; Ottenbacher, 1990; Park, Marascuilo, & Gaylord-Ross, 1990), most published single-case research (SCR) continues to rely on visual judgments, assisted by comparisons of phase means, medians, or percentages. Visual analysis also commonly includes judging the amount of data overlap between phases, which helps capture the important concept of data dispersion or variability around a center. A recent study of 124 published SCR datasets (Parker et al., 2005) found statistical analyses in only 11%, which is comparable to the 10% rate cited in earlier studies of 10 and 25 years ago (Busk & Marascuilo, 1992; Kratochwill & Brody, 1978). Thus, in acceptance of statistical analysis, the SCR field has changed little over recent decades. For the 75 multiple baseline designs (MBDs) sampled in this study, most authors (87%) relied solely on visual analysis, with phase percentages or means calculated, but no tests of differences between these indices. Data variability around the means and percents (e.g., standard deviations) was not presented, nor were reliabilities (standard error or confidence intervals [CIs]). The summary statistics served as nonessential additions to the primary visual analysis. Of the eight designs (11%) that were evaluated statistically, six used the student t Percentage of All Non-Overlapping Data (PAND):