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Percentage of All Non-Overlapping Data (PAND)

An Alternative to PND

Texas A&M University at College Station, rparker{at}tamu.edu

Shanna Hagan-Burke

Texas A&M University at College Station

Kimberly Vannest

Texas A&M University at College Station

Although single-case researchers are not accustomed to analyzing data statistically, standards for research and accountability from government and other funding agents are creating pressure for more objective, reliable data. In addition, "evidence-based interventions" movements in special education, clinical psychology, and school psychology imply reliable data summaries. Within special education, two heavily debated single-case research (SCR) statistical indices are "percentage of non-overlapping data" (PND) and the regression effect size, R². This article proposes a new index—PAND, the "percentage of all non-overlapping data"—to remedy deficiencies of both PND and R². PAND is closely related to the established effect size, Pearson's Phi , the "fourfold point correlation coefficient." The PAND/ Phi procedure is demonstrated and applied to 75 published multiple baseline designs to answer questions about typical effect sizes, relationships with PND and R², statistical power, and time efficiency. Confidence intervals and p values for Phi also are demonstrated. The findings are that PAND/ Phi and PND correlate equally well to R². However, only PAND/Phi could show adequate power for most of the multiple baseline designs sampled. The findings suggest that PAND/Phi may meet the requirement for a useful effect size for multiple baseline and other longer designs in SCR.

The Journal of Special Education, Vol. 40, No. 4, 194-204 (2007)
DOI: 10.1177/00224669070400040101


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