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)
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IATdata |
a dataframe with the following columns:
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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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | #### 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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