compute_iat {implicitMeasures}

Compute IAT D-score

Description

Compute D-score for the IAT according to different algorithms.

Usage

compute_iat(data, Dscore = c("d1", "d2", "d3", "d4", "d5", "d6"))

Arguments

data

Dataframe with class iat_clean.

Dscore

Character. Indicates which D-score to compute. For details on the algorithms, please refer to Greenwald et al. (2003).

Value

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.

Examples

# 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")