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.

1 |

`data` |
A single-case data frame or a list of single-case data frames. See |

`decreasing` |
If you expect data to be lower in the B phase, set |

`correction` |
The default |

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).

`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 |

`correction` |
Logical argument from function call (see |

Juergen Wilbert

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)
``` |

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