scan (version 0.20)

pand: Percentage of all non-overlapping data

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

pand(data, decreasing = FALSE, correction = TRUE)

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.

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).

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).

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.

See Also

overlapSC, describeSC, nap, pem, pet, pnd

Examples

# NOT RUN {
## 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)
# }