IATscores (version 0.2.7)

IATdescriptives: Summary statistics of reaction time and error

Description

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).

Usage

IATdescriptives(IATdata, byblock = FALSE)

Arguments

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

Value

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

Details

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.

References

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

See Also

Pretreatment

Examples

# 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))
IATdescriptives(IATdata)
# }