Description Usage Arguments Value Examples
View source: R/compute_sciat.R
Compute the D-score for the SC-IAT.
1 2 3 4 5 6 | compute_sciat(
data,
mappingA = "mappingA",
mappingB = "mappingB",
non_response = NULL
)
|
data |
Data frame with class |
mappingA |
String. Label identifying the mapping A of the SC-IAT in the
|
mappingB |
String. Label identifying the mapping B of the SC-IAT in the
|
non_response |
String. Labels of the trials identifying the
non-responses, a.k.a responses beyond the response time
window, as it was specified in |
A dataframe with class compute_sciat
. The number of rows of the
dataframe corresponds to the total number of participants. Variables
are defined as follows (the values are specific for each
participant):
participant
Respondents' IDs.
n_trial
Number of trial before data cleaning.
no_response
If there were any trials identifying the non
response, it indicates the number of non responses per each
participant. Otherwise, it is equal for all participants
("none"
).
nslow10000
Number of slow trials (> 10,000 ms).
out_accuracy
Indicates whether the participants had more
than 25 % of incorrect responses in at least one of the
critical blocks and hence should be eliminated ("out"
)
or not ("keep"
).
nfast400
Number of fast trials (< 400 ms).
nfast300
Number of fast trials (< 350 ms – deleted).
accuracy.mappingA
Proportion of correct responses in Mapping A.
accuracy.mappingB
Proportion of correct responses in mapping B.
RT_mean.MappingA
Mean response time in Mapping A.
RT_mean.MappingB
Mean response time in Mapping B.
cond_ord
Indicates the order with which the associative
conditions have been presented, either "MappingA_First"
or
"MappingB_First"
.
legendMappingA
Indicates the corresponding value of Mapping A in the original dataset.
legendMappingB
Indicates the corresponding value of Mapping B in the original dataset.
d_sciat
SC-IAT D.
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 | # calculate D for the SCIAT
data("raw_data") # load data
sciat_data <- clean_sciat(raw_data, sbj_id = "Participant",
block_id = "blockcode",
latency_id = "latency",
accuracy_id = "correct",
block_sciat_1 = c("test.sc_dark.Darkbad",
"test.sc_dark.Darkgood"),
block_sciat_2 = c("test.sc_milk.Milkbad",
"test.sc_milk.Milkgood"),
trial_id = "trialcode",
trial_eliminate = c("reminder",
"reminder1"))
sciat1 <- sciat_data[[1]] # compute D for the first SC-IAT
d_sciat1 <- compute_sciat(sciat1,
mappingA = "test.sc_dark.Darkbad",
mappingB = "test.sc_dark.Darkgood",
non_response = "alert")
head(d_sciat1) # dataframe containing the SC-IAT D of the of the
# first SC-IAT
sciat2 <- sciat_data[[2]] # Compute D for the second SC-IAT
d_sciat2 <- compute_sciat(sciat2,
mappingA = "test.sc_milk.Milkbad",
mappingB = "test.sc_milk.Milkgood",
non_response = "alert")
head(d_sciat2)
|
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