# pand: Percentage of all non-overlapping data In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

## Description

The pand function calculates the percentage of all non-overlapping data (PAND; Parker, Hagan-Burke, & Vannest, 2007), an index to quantify a level increase (or decrease) in performance after the onset of an intervention.

## Usage

 1 pand(data, decreasing = FALSE, correction = TRUE, phases = c("A", "B"))

## Arguments

 data A single-case data frame. See scdf to learn about this format. decreasing If you expect data to be lower in the B phase, set decreasing = TRUE. Default is decreasing = FALSE. correction The default correction = TRUE makes pand use a frequency matrix, which is corrected for ties. A tie is counted as the half of a measurement in both phases. Set correction = FALSE to use the uncorrected matrix, which is not recommended. phases A vector of two characters or numbers indicating the two phases that should be compared. E.g., phases = c("A","C") or phases = c(2,4) for comparing the second to the fourth phase. Phases could be combined by providing a list with two elements. E.g., phases = list(A = c(1,3), B = c(2,4)) will compare phases 1 and 3 (as A) against 2 and 4 (as B). Default is phases = c("A","B").

## Details

The PAND indicates nonoverlap between phase A and B data (like PND), but uses all data and is therefore not based on one single (probably unrepresentative) datapoint. Furthermore, PAND allows the comparison of real and expected associations (Chi-square test) and estimation of the effect size Phi, which equals Pearsons r for dichotomous data. Thus, phi-Square is the amount of explained variance. The original procedure for computing the PAND (Parker, Hagan-Burke, & Vannest, 2007) does not account for ambivalent datapoints (ties). The newer NAP overcomes this problem and has better precision-power (Parker, Vannest, & Davis, 2014).

## Value

 PAND Percentage of all non-overlapping data. phi Effect size Phi based on expected and observed values. POD Percentage of overlapping data points. OD Number of overlapping data points. n Number of data points. N Number of cases. nA Number of data points in phase A. nB Number of data points in phase B. pA Percentage of data points in phase A. pB Percentage of data points in phase B. matrix 2x2 frequency matrix of phase A and B comparisons. matrix.counts 2x2 counts matrix of phase A and B comparisons. correlation A list of the correlation values: statistic, parameter, p.value, estimate, null.value, alternative, method, data.name, correction. correction Logical argument from function call (see Arguments above).

Juergen Wilbert

## References

Parker, R. I., Hagan-Burke, S., & Vannest, K. (2007). Percentage of All Non-Overlapping Data (PAND): An Alternative to PND. The Journal of Special Education, 40, 194-204.

Parker, R. I., & Vannest, K. (2009). An Improved Effect Size for Single-Case Research: Nonoverlap of All Pairs. Behavior Therapy, 40, 357-367.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 ## Calculate the PAND for a MMBD over three cases gunnar <- scdf(c(2,3,1,5,3,4,2,6,4,7), B.start = 5) birgit <- scdf(c(3,3,2,4,7,4,2,1,4,7), B.start = 4) bodo <- scdf(c(2,3,4,5,3,4,7,6,8,7), B.start = 6) mbd <- c(gunnar, birgit, bodo) pand(mbd) pand(bodo) ## Calculate the PAND with an expected decrease of phase B scores cubs <- scdf(c(20,22,24,17,21,13,10,9,20,9,18), B.start = 5) pand(cubs, decreasing = TRUE)

scan documentation built on June 20, 2018, 3 p.m.