TestRetest: Test-retest reliability

Description

Compute test-retest reliability for IAT with 2 observations for each subject

Usage

TestRetest(IATdata, ...)
TestRetest.D2(IATdata, ...)
TestRetest.D5(IATdata, ...)
TestRetest.D6(IATdata, ...)
TestRetest.D2SWND(IATdata, ...)
TestRetest.D5SWND(IATdata, ...)
TestRetest.D6SWND(IATdata, ...)

Arguments

IATdata

same as RobustScores, but with the additional column "session". session distinguishes the trials of the first session and those of the second session. It is typically numerical, having value 1 for the first session and 2 for the second. Functions TestRetest.D2 etc. are wrappers that allow computing reliability for some common types of scores. See RobustScores.

…

other parameters to be passed to RobustScores

Value

algorithm

The name of the algorithm, see RobustScores for the convention adopted for naming the algorithms

testretest

The test-retest reliability for each algorithm

Details

It computes the scores for the test and for the retest using RobustScores, the output is just the correlation among the scores in the two sessions.

Examples

Run this code

# NOT RUN {
#### generate random IAT data ####
set.seed(1234)
rawIATdata <- data.frame(
  # ID of each participant (N = 10)
  ID = rep(1:10, each = 180), 
  # seven-block structure, as in Greenwald, Nosek & Banaji (2003)
  # block 1 = target discrimination (e.g., Bush vs. Gore items)
  # block 2 = attribute discrimination (e.g., Pleasant words vs. unpleasant)
  # block 3 = combined practice (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 4 = combined critical  (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 5 = reversed target discrimination (e.g., Gore vs. Bush)
  # block 6 = reversed combined practice (e.g., Gore + pleasant vs. Bush + unpleasant)
  # block 7 = reversed combined critical (e.g., Gore + pleasant vs. Bush + unpleasant)
  block = rep(c(rep(1:3, each = 20),
                rep(4, 40),
                rep(5:6, each = 20),
                rep(7, 40)), 10),
 # expected proportion of errors = 10 percent
  correct = sample(c(0, 1), size = 1800, replace = TRUE, prob = c(.2, .8)),
  # reaction times are generated from a mix of two chi2 distributions,
  # one centered on 550ms and one on 100ms to simulate fast latencies
  latency = round(sample(c(rchisq(1500, df = 1, ncp = 550),
                           rchisq(300, df = 1, ncp = 100)), 1800)))

# add some IAT effect by making trials longer in block 6 and 7
rawIATdata[rawIATdata$block >= 6, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6, "latency"] + 100
  
# add some more effect for subjects 1 to 5
rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] + 100
  
#### pretreat IAT data using function Pretreatment ####
IATdata <- Pretreatment(rawIATdata,
                             label_subject = "ID",
                          label_latency = "latency",
                          label_accuracy = "correct",
                          label_block = "block",
                          block_pair1 = c(3, 4),
                          block_pair2 = c(6, 7),
                          label_praccrit = "block",
                          block_prac = c(3, 6),
                          block_crit = c(4, 7))
                          
# Add a column representing the session in IATdata                    
IATdata$session <- rep(c(1,2), nrow(IATdata)/2)
                          

#### Compute reliability for Greenwald et al.'s (2003) D2, D5, and D6 ####
# All scores are computed both with the TestRetest and with
# the wrappers TestRetest.D2, TestRetest.D5, and TestRetest.D6.


# D2 scores
TestRetest.D2(IATdata, verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "ignore",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

# D5 scores
TestRetest.D5(IATdata,
             verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "recode",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

# D6 scores
TestRetest.D6(IATdata, verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "recode600",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

#### Compute reliability for improved scores by Richetin et al. (2015, p. 20) ####
# All scores are computed both with the TestRetest and with
# the wrappers TestRetest.D2SWND, TestRetest.D5SWND, and TestRetest.D6SWND.
# Results are identical

# D2SWND scores
TestRetest.D2SWND(IATdata, verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "wins10",
             P2 = "ignore",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

# D5_SWND scores
TestRetest.D5SWND(IATdata, verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "wins10",
             P2 = "recode",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

# D6_SWND scores
TestRetest.D6SWND(IATdata, verbose = FALSE)
TestRetest(IATdata = IATdata,
             P1 = "wins10",
             P2 = "recode600",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

# }