compute_sciat {implicitMeasures}

Compute the D-score for the SC-IAT

Description

Compute the D-score for the SC-IAT.

Usage

compute_sciat(
  data,
  mappingA = "mappingA",
  mappingB = "mappingB",
  non_response = NULL
)

Arguments

data

Data frame with class clean_sciat.

mappingA

String. Label identifying the mapping A of the SC-IAT in the block_id variable.

mappingB

String. Label identifying the mapping B of the SC-IAT in the block_id variable.

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 trial_id (if included).

Value

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.

Examples

# 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)