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

2. Percent Non-overlapping Data (PND)
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.

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

4. PND - Concerns
2. Ignores Baseline Trend

5. PND - Concerns
3. Ceiling Effect

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

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

8. Percent Exceeding Median(PEM)
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.

9. Percent Exceeding Median(PEM)
PND
PEM
Stable - +
2. Account for Trends
3. Sensitive to Size of Effect
4. Known Sampling Distribution
5. Comparability

10. Extended Celeration Line (ECL or PEM-T)
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.

11. ECL Stable - + 2. Account for Trends +a 3. Sensitive to Size of Effect
PND
PEM
ECL
Stable - +
2. Account for Trends +a
3. Sensitive to Size of Effect
4. Known Sampling Distribution
5. Comparability
aAssuming trend is linear and can be extrapolated

12. NAP
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.

13. NAP Stable - + 2. Account for Trends +a 3. Sensitive to Size of Effect
PND
PEM
ECL
NAP
Stable - +
2. Account for Trends +a
3. Sensitive to Size of Effect
4. Known Sampling Distribution +b
5. Comparability
aAssuming trend is linear and can be extrapolated
bAssuming independence

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

15. TauU Stable - + 2. Account for Trends +a
PND
PEM
ECL
NAP
TauU
Stable - +
2. Account for Trends +a
3. Sensitive to Size of Effect
4. Known Sampling Distribution +b
5. Comparability
aAssuming trend is linear and can be extrapolated
bAssuming independence

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. TauUadj cSome technical questions about amount of adjustment Stable -
PND
PEM
ECL
NAP
TauU
TauUadj
Stable - + -c
2. Account for Trends +a +d
3. Sensitive to Size of Effect
4. Known Sampling Distribution +b -e
5. Comparability
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

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,
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,
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, Scruggs, T. E., Mastopieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education, 8,
Scruggs, T. E., Mastopieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education, 8,
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,