ird {scan}

IRD - Improvement rate difference

Description

ird() calculates the robust improvement rate difference as proposed by Parker and colleagues (2011).

Usage

ird(data, dvar, pvar, decreasing = FALSE, phases = c(1, 2))

## S3 method for class 'sc_ird'
print(x, digits = 3, ...)

Arguments

data

A single-case data frame. See scdf() to learn about this format.

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 decreasing = TRUE. Default is decreasing = FALSE.

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(1,2).

x

An object returned by ird()

digits

The minimum number of significant digits to be use.

...

Further arguments passed to the function.

Details

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.

Functions

• print(sc_ird): Print results

References

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