ird | R Documentation |
ird()
calculates the robust improvement rate difference as proposed by
Parker and colleagues (2011).
ird(data, dvar, pvar, decreasing = FALSE, phases = c(1, 2))
## S3 method for class 'sc_ird'
print(x, digits = 3, ...)
data |
A single-case data frame. See |
dvar |
Character string with the name of the dependent variable. Defaults to the attributes in the scdf file. |
pvar |
Character string with the name of the phase variable. Defaults to the attributes in the scdf file. |
decreasing |
If you expect data to be lower in the B phase, set
|
phases |
A vector of two characters or numbers indicating the two phases
that should be compared. E.g., |
x |
An object returned by |
digits |
The minimum number of significant digits to be use. |
... |
Further arguments passed to the function. |
The adaptation of the improvement rate difference for single-case phase comparisons was developed by Parker and colleagues (2009). A variation called robust improvement rate difference was proposed by Parker and colleagues in 2011. This function calculates the robust improvement rate difference. It follows the formula suggested by Pustejovsky (2019). For a multiple case design, ird is based on the overall improvement rate of all cases which is the average of the irds for each case.
print(sc_ird)
: Print results
Parker, R. I., Vannest, K. J., & Brown, L. (2009). The improvement rate difference for single-case research. Exceptional Children, 75(2), 135-150.
Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Effect Size in Single-Case Research: A Review of Nine Nonoverlap Techniques. Behavior Modification, 35(4), 303-322. https://doi.org/10.1177/0145445511399147
Pustejovsky, J. E. (2019). Procedural sensitivities of effect sizes for single-case designs with directly observed behavioral outcome measures. Psychological Methods, 24(2), 217-235. https://doi.org/10.1037/met0000179
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