IATdescriptives: Summary statistics of reaction time and error
In IATscores: Implicit Association Test Scores Using Robust Statistics

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/IATdescriptives.R

Provides several summary statistics for reaction times and errors, by subject and by block. If by block, only two critical blocks, pair1 and pair2, are considered. See function Pretreatment).

1	IATdescriptives(IATdata, byblock = FALSE)

IATdata

a dataframe with the following columns:

subject: (factor or coercible to factor). Univocally identifies a participant.
correct: (logical). has value TRUE or 1 if the trial was answered correctly, FALSE or 0 otherwise.
latency: (numeric). Response latency, in ms.
blockcode: (factor or string). Can assume only two values, "pair1" and "pair2". "pair1" is for one critical block and "pair2" is the other critical block.
praccrit. (factor, optional). Can assume only two values, "prac" is for practice combined categorization block and "crit" is for critical combined categorization block. In a IAT with 60 trials for each double categorization block, the first 20 are sometimes administered as practice block, the other 40 as critical.

byblock

If TRUE, summary statistics are returned separately for the two critical blocks, pair1 and pair2

These summary statistics are used sometimes to define exclusion criteria. For example, Greenwald, Nosek, & Banaji's (2003) improved algorithm suggests to eliminate subjects for whom more than 10 percent trials have latency less than 300ms.

`Ntrials`	number of trials
`Nmissing_latency`	number of trials in which latency information is missing
`Nmissing_accuracy`	number of trials in which accuracy information is missing
`Prop_error`	proportion of error trials
`M_latency`	mean latency
`SD_latency`	SD of latency
`min_latency`	minimum value of latency
`max_latency`	maximum value of latency
`Prop_latency300`	proportion of latencies faster than 300 ms
`Prop_latency400`	proportion of latencies faster than 400 ms
`Prop_latency10s`	proportion of latencies slower than 10 seconds

Giulio Costantini

Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197-216. doi:10.1037/0022-3514.85.2.197

Pretreatment

#### 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))
IATdescriptives(IATdata)