compute_sciat: Compute the D-score for the SC-IAT
In implicitMeasures: Compute Scores for Different Implicit Measures

compute_sciat

R Documentation

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)

implicitMeasures documentation built on March 18, 2022, 5:17 p.m.