compute_iat | R Documentation |
Compute D-score for the IAT according to different algorithms.
compute_iat(data, Dscore = c("d1", "d2", "d3", "d4", "d5", "d6"))
data |
Dataframe with class |
Dscore |
Character. Indicates which D-score to compute. For details on the algorithms, please refer to Greenwald et al. (2003). |
Dataframe with class "dscore"
. 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 trails before data cleaning.
nslow10000
Number of slow trials (> 10,000 ms).
nfast400
Number of fast trials (< 400 ms).
nfast300
Number of fast trials (< 300 ms).
accuracy.practice_MappingA
Proportion of correct responses in practice block of Mapping A.
accuracy.practice_MappingB
Proportion of correct responses in practice block of Mapping B.
accuracy.test_MappingA
Proportion of correct responses in test block of Mapping A.
accuracy.test_MappingB
Proportion of correct responses in test block of Mapping B.
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.
mean_practice_MappingA
Mean response time in practice block of Mapping A.
mean_practice_MappingB
Mean response time in practice block of Mapping B.
mean_test_MappingA
Mean response time in test block of Mapping A.
mean_test_MappingB
Mean response time in test block of Mapping B.
d_practice_dX
D-scores compute_iat on the practice blocks. The X stands for the selected D-score procedure.
d_test_dX
D-scores compute_iat on the test blocks. The X stands for the selected D-score procedure.
dscore_dX
The average D-score for the practice and test D-scores. The X stands for the selected D-score procedure.
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.
# compute D-score 2 for the IAT data ### data("raw_data") # import data iat_cleandata <- clean_iat(raw_data, sbj_id = "Participant", block_id = "blockcode", mapA_practice = "practice.iat.Milkbad", mapA_test = "test.iat.Milkbad", mapB_practice = "practice.iat.Milkgood", mapB_test = "test.iat.Milkgood", latency_id = "latency", accuracy_id = "correct", trial_id = "trialcode", trial_eliminate = c("reminder", "reminder1"), demo_id = "blockcode", trial_demo = "demo") iat_data <- iat_cleandata[[1]] # calculate D-score iat_dscore <- compute_iat(iat_data, Dscore = "d2")
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