UDT {singcar}

Unstandardised Difference Test

Description

A test on the discrepancy between two tasks in a single case, by comparison to the mean of discrepancies of the same two tasks in a control sample. Use only when the two tasks are measured on the same scale with the same underlying distribution because no standardisation is performed on task scores. As a rule-of-thumb, the UDT may be applicable to pairs of tasks for which it would be sensible to perform a paired t-test within the control group. Calculates however a standardised effect size in the same manner as RSDT(). This is original behaviour from Crawford and Garthwaite (2005) but might not be appropriate. So use this standardised effect size with caution. Calculates a standardised effect size 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 and respective confidence intervals.

Usage

UDT(
  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"),
  conf_int = TRUE,
  conf_level = 0.95,
  conf_int_spec = 0.01,
  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 and/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.

conf_int

Initiates a search algorithm for finding confidence intervals. Defaults to TRUE, set to FALSE for faster calculation (e.g. for simulations).

conf_level

Level of confidence for intervals, defaults to 95%.

conf_int_spec

The size of iterative steps for calculating confidence intervals. Smaller values gives more precise intervals but takes longer to calculate. Defaults to a specificity of 0.01.

na.rm

Remove NAs from controls.

Details

Running UDT is equivalent to running TD on discrepancy scores making it possible to run unstandardised tests with covariates by applying BTD_cov to discrepancy scores.

Value

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

statistic	the t-statistic.
parameter	the degrees of freedom for the t-statistic.
p.value	the p-value of the test.
estimate	unstandardised case scores, task difference and pont estimate of proportion control population expected to above or below the observed task difference.
control.desc	named numerical with descriptive statistics of the control samples.
null.value	the value of the difference 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

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

UDT(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"])