scan (version 0.53)

cdc: 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 = "OLS",
  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("A","B").

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

-

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

Examples

# NOT RUN {
## Apply the CDC method (standard OLS line)
design <- design_rSC(n = 1, slope = 0.2)
dat <- rSC(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)


# }