crawford_dissociation.test: Crawford-Howell (1998) modified t-test for testing difference between a patient<U+00E2><U+20AC><U+2122>s performance on two tasks.

Description

Assessing dissociation between processes is a fundamental part of clinical neuropsychology. However, while the detection of suspected impairments is a fundamental feature of single-case studies, evidence of an impairment on a given task usually becomes of theoretical interest only if it is observed in the context of less impaired or normal performance on other tasks. Crawford and Garthwaite (2012) demonstrate that the Crawford-Howell (1998) t-test for dissociation is a better approach (in terms of controlling Type I error rate) than other commonly-used alternatives. .

Usage

crawford_dissociation.test(
  case_X,
  case_Y,
  controls_X,
  controls_Y,
  verbose = TRUE
)

Arguments

case_X

Single value (patient's score on test X).

case_Y

Single value (patient's score on test Y).

controls_X

Vector of values (control's scores of X).

controls_Y

Vector of values (control's scores of Y).

verbose

True or False. Prints the interpretation text.

Value

Returns a data frame containing the t-value, degrees of freedom, and p-value. If significant, the dissociation between test X and test Y is significant.

Examples

Run this code

# NOT RUN {
library(psycho)

case_X <- 142
case_Y <- 7
controls_X <- c(100, 125, 89, 105, 109, 99)
controls_Y <- c(7, 8, 9, 6, 7, 10)

crawford_dissociation.test(case_X, case_Y, controls_X, controls_Y)
# }

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