Conservative Dual-Criterion Method

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`	-

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

Author

Timo Lueke

Examples


## Apply the CDC method (standard OLS line)
design <- design(n = 1, slope = 0.2)
dat <- random_scdf(design, seed = 42)
cdc(dat)
#> Conservative Dual Criterion
#> 
#> N cases =  1 
#> 
#>   Case nB improve nB binom p    CDC Evaluation
#>  Case1         14 15   <.001 systematic change
#> 
#> Assuming an expected increase in phase B.
#> Alternative hypothesis (Binomial test): true probability > 50%

## Apply the CDC with Koenig's bi-split and an expected decrease in phase B.
cdc(exampleAB_decreasing, decreasing = TRUE, trend_method = "bisplit")
#> Conservative Dual Criterion
#> 
#> N cases =  3 
#> 
#>   Case nB improve nB binom p    CDC Evaluation
#>  Peter          7 13     .50         no change
#>   Tony         11 12    <.01 systematic change
#>  Bruce         14 14   <.001 systematic change
#> 
#> Assuming an expected decrease in phase B.
#> Alternative hypothesis (Binomial test): true probability < 50%
#> Overall evaluation of all MBD instances:   no change 

## Apply the CDC with Tukey's tri-split, comparing the first and fourth phase
cdc(exampleABAB, trend_method = "trisplit", phases = c(1,4))
#> Conservative Dual Criterion
#> 
#> N cases =  3 
#> 
#>     Case nB improve nB binom p    CDC Evaluation
#>   Howard         10 10   <.001 systematic change
#>  Sheldon          4 10     .82         no change
#>  Leonard          7  7    <.01 systematic change
#> 
#> Assuming an expected increase in phase B.
#> Alternative hypothesis (Binomial test): true probability > 50%
#> Overall evaluation of all MBD instances:   no change 

## Apply the Dual-Criterion (DC) method (i.e., mean and trend without
##shifting).
cdc(
 exampleAB_decreasing,
 decreasing = TRUE,
 trend_method = "bisplit",
 conservative = 0
)
#> Conservative Dual Criterion
#> 
#> N cases =  3 
#> 
#>   Case nB improve nB binom p    CDC Evaluation
#>  Peter          7 13     .50         no change
#>   Tony         12 12   <.001 systematic change
#>  Bruce         14 14   <.001 systematic change
#> 
#> Assuming an expected decrease in phase B.
#> Alternative hypothesis (Binomial test): true probability < 50%
#> Overall evaluation of all MBD instances:   no change