RSDT {singcar}

Revised Standardised Difference Test

Description

A test on the discrepancy between two tasks in a single case, by comparison to the discrepancy of means in the same two tasks in a control sample. Standardises task scores as well as task discrepancy, so the tasks do not need to be measured on the same scale. Calculates a standardised effect size (Z-DCC) of task discrepancy as well as a point estimate of the proportion of the control population that would be expected to show a more extreme discrepancy. Developed by Crawford and Garthwaite (2005).

Usage

RSDT(
  case_a,
  case_b,
  controls_a,
  controls_b,
  sd_a = NULL,
  sd_b = NULL,
  sample_size = NULL,
  r_ab = NULL,
  alternative = c("two.sided", "greater", "less"),
  na.rm = FALSE
)

Arguments

case_a

Case's score on task A.

case_b

Case's score on task B.

controls_a

Controls' scores on task A. Takes either a vector of observations or a single value interpreted as mean. Note: you can supply a vector as input for task A while mean and SD for task B.

controls_b

Controls' scores on task B. Takes either a vector of observations or a single value interpreted as mean. Note: you can supply a vector as input for task B while mean and SD for task A.

sd_a

If single value for task A is given as input you must supply the standard deviation of the sample.

sd_b

If single value for task B is given as input you must supply the standard deviation of the sample.

sample_size

If A or B is given as mean and SD you must supply the sample size. If controls_a is given as vector and controls_b as mean and SD, sample_size must equal the number of observations in controls_a.

r_ab

If A or B is given as mean and SD you must supply the correlation between the tasks.

alternative

A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter. Since the direction of the expected effect depends on which task is set as A and which is set as B, be very careful if changing this parameter.

na.rm

Remove NAs from controls.

Value

A list with class "htest" containing the following components:

statistic	Returns the value of a approximate t-statistic, however, because of the underlying equation, it cannot be negative. See effect direction from Z-DCC.
parameter	the degrees of freedom for the t-statistic.
p.value	the p-value for the test.
estimate	case scores expressed as z-scores on task A and Y. Standardised effect size (Z-DCC) of task difference between case and controls and point estimate of the proportion of the control population estimated to show a more extreme task discrepancy.
sample.size	the size of the control sample
null.value	the value of the discrepancy under the null hypothesis.
alternative	a character string describing the alternative hypothesis.
method	a character string indicating what type of test was performed.
data.name	a character string giving the name(s) of the data

References

Crawford, J. R., & Garthwaite, P. H. (2005). Testing for Suspected Impairments and Dissociations in Single-Case Studies in Neuropsychology: Evaluation of Alternatives Using Monte Carlo Simulations and Revised Tests for Dissociations. Neuropsychology, 19(3), 318 - 331. doi:10.1037/0894-4105.19.3.318

Examples

RSDT(-3.857, -1.875, controls_a = 0, controls_b = 0, sd_a = 1,
sd_b = 1, sample_size = 20, r_ab = 0.68)

RSDT(case_a = size_weight_illusion[1, "V_SWI"], case_b = size_weight_illusion[1, "K_SWI"],
 controls_a = size_weight_illusion[-1, "V_SWI"], controls_b = size_weight_illusion[-1, "K_SWI"])