cdc: Conservative Dual-Criterion Method

In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Conservative Dual-Criterion Method

Description

The cdc() function applies the Conservative Dual-Criterion Method (Fisher, Kelley, & Lomas, 2003) to scdf objects. It compares phase B data points to both phase A mean and trend (OLS, bi-split, tri-split) with an additional increase/decrease of .25 SD. A binomial test against a 50/50 distribution is computed and p-values below .05 are labeled "systematic change".

Usage

cdc(
  data,
  dvar,
  pvar,
  mvar,
  decreasing = FALSE,
  trend_method = c("OLS", "bisplit", "trisplit"),
  conservative = 0.25,
  phases = c(1, 2)
)

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.
mvar	Character string with the name of the measurement time 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.
trend_method	Method used to calculate the trend line. Default is trend_method = "OLS". Possible values are: "OLS", "bisplit", and "trisplit". "bisplit", and "trisplit" should only be used for cases with at least five data-points in both relevant phases.
conservative	The CDC method adjusts the original mean and trend lines by adding (expected increase) or subtracting (expected decrease) an additional .25 SD before evaluating phase B data. Default is the CDC method with conservative = .25. To apply the Dual-Criterion (DC) method, set conservative = 0.
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).

Value

cdc	CDC Evaluation based on a p-value below .05.
cdc_exc	Number of phase B datapoints indicating expected change.
cdc_nb	Number of phase B datapoints.
cdc_p	P value of Binomial Test.
cdc_all	Overall CDC Evaluation based on all instances/cases of a Multiple Baseline Design.
N	Number of cases.
decreasing	Logical argument from function call (see Arguments above).
conservative	Numeric argument from function call (see Arguments above).
case_names	Assigned name of single-case.
phases	-

Author(s)

Timo Lueke

References

Fisher, W. W., Kelley, M. E., & Lomas, J. E. (2003). Visual Aids and Structured Criteria for Improving Visual Inspection and Interpretation of Single-Case Designs. Journal of Applied Behavior Analysis, 36, 387-406. https://doi.org/10.1901/jaba.2003.36-387

See Also

Other overlap functions: nap(), overlap(), pand(), pem(), pet(), pnd(), tau_u()

Examples

## Apply the CDC method (standard OLS line)
design <- design(n = 1, slope = 0.2)
dat <- random_scdf(design, seed = 42)
cdc(dat)

## Apply the CDC with Koenig's bi-split and an expected decrease in phase B.
cdc(exampleAB_decreasing, decreasing = TRUE, trend_method = "bisplit")

## Apply the CDC with Tukey's tri-split, comparing the first and fourth phase
cdc(exampleABAB, trend_method = "trisplit", phases = c(1,4))

## Apply the Dual-Criterion (DC) method (i.e., mean and trend without
##shifting).
cdc(
 exampleAB_decreasing,
 decreasing = TRUE,
 trend_method = "bisplit",
 conservative = 0
)

scan documentation built on July 1, 2024, 9:07 a.m.