View source: R/IATdescriptives.R
IATdescriptives | R Documentation |
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
).
IATdescriptives(IATdata, byblock = FALSE)
IATdata |
a dataframe with the following columns:
|
byblock |
If |
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)
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