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Non-Overlap Measures

This article explores various non-overlap measures, such as PND, PEM, ECL, NAP, TauU, and TauUadj, for analyzing single-case research data. It discusses their strengths, limitations, and alternative effect sizes.

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Non-Overlap Measures

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  1. Non-Overlap Measures PND PEM ECL (PEM-T) NAP TauU TauUadj

  2. If anticipating an increase, find the highest data point in the A phase, and then find the percent of the B phase data points that exceed it. Percent Non-overlapping Data (PND)

  3. 1. Instability • Sensitive to Outliers • Sensitive to Number of Baseline Observations PND - Concerns

  4. 2. Ignores Baseline Trend PND - Concerns

  5. 3. Ceiling Effect PND - Concerns

  6. 4. No known sampling distribution • Cannot weight effect sizes based on precision • 5. Not comparable to group effect sizes • Limits audience PND - Concerns

  7. A series of other non-overlap effect sizes developed to overcome noted concerns with PND Alternative Effect Sizes: Nonparametric

  8. If anticipating an increase, find the median of the A phase, and then find the percent of the B phase data points that exceed it. Percent Exceeding Median(PEM)

  9. Percent Exceeding Median(PEM)

  10. If anticipating an increase, find the celebration line of the A phase, extend it, and then find the percent of the B phase data points that exceed it. Extended Celeration Line (ECL or PEM-T)

  11. aAssuming trend is linear and can be extrapolated ECL

  12. Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = nA*nB). Count the number of Positive (P), Negative (N), and Tied (T) pairs. NAP

  13. aAssuming trend is linear and can be extrapolated • bAssuming independence NAP

  14. TauU is closely related to NAP • If no ties then • TauU is scaled from -1 to 1 TauU

  15. aAssuming trend is linear and can be extrapolated • bAssuming independence TauU

  16. TauUadj To adjust TauU for baseline trend, each baseline observation can be paired with all later baseline observations (nA*(nA-1)/2). Then compute baseline trend:

  17. aAssuming trend is linear and can be extrapolated • bAssuming independence • CTrend adjustment introduces dependency on baseline length • dSome technical questions about the amount of adjustment • eTrend adjustment alters sampling distribution TauUadj cSome technical questions about amount of adjustment

  18. http://www.singlecaseresearch.org/calculators/tau-u

  19. https://jepusto.shinyapps.io/SCD-effect-sizes/

  20. https://jepusto.shinyapps.io/SCD-effect-sizes/

  21. References Ma, H.-H., (2006). An alternative method for quantitative synthesis of single-subject research: Percentage of data points exceeding the median. Behavior Modification, 30, 598-617. Parker, R. I., Vannest, K. J., & Davis, J. L. (2014). Non-overlap analysis for single-case research. In T. R. Kratochwill & J. R. Levin (Eds.) Single-case intervention research: Methodological and statistical advances. Washington DC: APA. Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Nine non-overlap techniques for single case research. Behavior Modification, 35, 303-322. Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S (2011). Combining non-overlap and trend for single-case research: Tau-U. Behavior Therapy, 42, 284-299. Scruggs, T. E., Mastopieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education, 8, 24-33. Scruggs, T. E., Mastopieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education, 8, 24-33. Wolery, M., Busick, M., Reichow, B., & Barton, E. E. (2010). Comparison of overlap methods for quantitatively synthesizing single-subject data. The Journal of Special Education, 44, 18-28.

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