descript_d | R Documentation |
Descriptive statistics for the IAT D-score or the SC-IAT D.
descript_d(data, latex = FALSE)
data |
Dataframe with either class |
latex |
Logical. If |
Dataframe, containing the mean, s.d., minimum and maximum of the IAT
(D-score
, D-practice
, and D-test
) or the SC-IAT
(D-Sciat
, RT.MappingA
, RT.MappingB
).
# 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") descript_d(iat_dscore) # descriptive statistics for the IAT # 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") descript_d(d_sciat1, latex = TRUE) # descriptive statistics for the SC-IAT in latex # format
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