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

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Presentation transcript:

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

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.

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

PND - Concerns 2. Ignores Baseline Trend

PND - Concerns 3. Ceiling Effect

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

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

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.

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

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.

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

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.

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

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

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

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:

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

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

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

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

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.