# 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 or a list of single-case data frames. See `makeSCDF` 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` -

## 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.

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