Package 'implicitMeasures' reference manual

Title:	Compute Scores for Different Implicit Measures
Description:	A tool for computing the scores for the Implicit Association Test (IAT; Greenwald, McGhee & Schwartz (1998) <doi:10.1037/0022-3514.74.6.1464>) and the Single Category-IAT (SC-IAT: Karpinski & Steinman (2006) <doi:10.1037/0022-3514.91.1.16>). Functions for preparing the data (both for the IAT and the SC-IAT), plotting the results, and obtaining a table with the scores of implicit measures descriptive statistics are provided.
Authors:	Ottavia M. Epifania [aut, cre], Pasquale Anselmi [ctb], Egidio Robusto [ctb]
Maintainer:	Ottavia M. Epifania <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.1
Built:	2025-02-05 03:25:33 UTC
Source:	https://github.com/ottaviae/implicitmeasures

Prepare and clean IAT data.

Description

Select IAT blocks for the D-score computation and eventually save demographic data.

Usage

clean_iat(
  data,
  sbj_id = "participant",
  block_id = "blockcode",
  mapA_practice = "practice_MappingA",
  mapA_test = "test_MappingA",
  mapB_practice = "practice_MappingB",
  mapB_test = "test_MappingB",
  latency_id = "latency",
  accuracy_id = "correct",
  trial_id = NULL,
  trial_eliminate = NULL,
  demo_id = NULL,
  trial_demo = NULL
)
clean_iat(
  data,
  sbj_id = "participant",
  block_id = "blockcode",
  mapA_practice = "practice_MappingA",
  mapA_test = "test_MappingA",
  mapB_practice = "practice_MappingB",
  mapB_test = "test_MappingB",
  latency_id = "latency",
  accuracy_id = "correct",
  trial_id = NULL,
  trial_eliminate = NULL,
  demo_id = NULL,
  trial_demo = NULL
)

Arguments

`data`	Dataframe containing IAT data.
`sbj_id`	Column identifying participants' IDs. This variable can be a `character`, `numeric`, or `factor`.
`block_id`	String. Column identifying IAT blocks. The `block_id` variable should be a `factor` with each level identifying an IAT block.
`mapA_practice`	String. Label for the practice blocks of Mapping A (as it appears in the `block_id` variable).
`mapA_test`	String. Label for the test blocks of Mapping A (as it appears in the `block_id` variable).
`mapB_practice`	String. Label for the practice blocks of Mapping B (as it appears in the `block_id` variable).
`mapB_test`	String. Label for the test blocks of Mapping B (as it appears in the `block_id` variable).
`latency_id`	String. Column identifying response times (in millisecond). If the IAT had a built-in correction, latencies of the incorrect responses should be those inflated with the built-in correction.
`accuracy_id`	String. Column identifying the IAT accuracy responses. The `accuracy_id` variable should be a numeric variable identifying the correct responses (with 1) and the incorrect responses (with 0).
`trial_id`	Character. Column identifying the trials. Specify this only if you want to delete some specific trials.
`trial_eliminate`	Character or character vector. Label(s) identifying the trials in `trial_id` to eliminate.
`demo_id`	Character. Column identifying demographic blocks. It can be the same as `block_id`.
`trial_demo`	Character or character vector identifying the name of the blocks in `demo_id` containing the demographic information.

Value

List of dataframe.

data_keep: Dataframe with class iat_clean. The dataframe contains the data of the blocks specified in mapA_practice, mapA_test, mapB_practice, mapB_test. If you have specified the trials to eliminate through trial_eliminate, data_keep will contain the already cleaned dataset. This dataset should be passed to the computeD function.
data_eliminate: Dataframe containing all the discarded blocks and trials.
data_demo: Dataframe containing demographic variables. It will be present only if you specified the demo_id and trial_demo arguments.

Examples

data("raw_data") # load 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]] # select the first element of the list (IAT data)
head(iat_data)
demo_data <- iat_cleandata[[3]] # select the third element of the list
                            # (demographic data)
head(demo_data)
data("raw_data") # load 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]] # select the first element of the list (IAT data)
head(iat_data)
demo_data <- iat_cleandata[[3]] # select the third element of the list
                            # (demographic data)
head(demo_data)

Prepare and clean SC-IAT data

Description

Select the SC-IAT blocks, for either one or two SC-IATs. Eventually save demographic data.

Usage

clean_sciat(
  data,
  sbj_id = "participant",
  block_id = "blockcode",
  accuracy_id = "correct",
  latency_id = "latency",
  block_sciat_1 = NULL,
  block_sciat_2 = NULL,
  trial_id = NULL,
  trial_eliminate = NULL,
  demo_id = NULL,
  trial_demo = NULL
)
clean_sciat(
  data,
  sbj_id = "participant",
  block_id = "blockcode",
  accuracy_id = "correct",
  latency_id = "latency",
  block_sciat_1 = NULL,
  block_sciat_2 = NULL,
  trial_id = NULL,
  trial_eliminate = NULL,
  demo_id = NULL,
  trial_demo = NULL
)

Arguments

`data`	Dataframe containing SC-IAT data.
`sbj_id`	Column identifying participants' IDs. This variable can be a `character`, `numeric`, or `factor`.
`block_id`	String. Column identifying SC-IAT blocks. The `block_id` variable should be a `factor` with each level identifying a SC-IAT block.
`accuracy_id`	String. Column identifying the IAT accuracy responses. The `accuracy_id` variable should be a numeric variable identifying the correct responses (with 1) and the incorrect responses (with 0).
`latency_id`	String. Column identifying response times (in millisecond).
`block_sciat_1`	Character or character vector. Labels identifying the first SC-IAT blocks as they are named in the `block_id`.
`block_sciat_2`	Character or character vector. Labels identifying the second (if present) SC-IAT blocks as they are named in the `block_id`.
`trial_id`	Character. Column identifying the trials. Specify this only if you want to delete some specific trials. If a response window was used for the SC-IAT administration the label of the non-response must be included in this variable.
`trial_eliminate`	Character or character vector. Labels of the trials to eliminate in the `trial_id` to eliminate (NOTE: don't use this command to delete the responses exceeding the response time window).
`demo_id`	Character. Character. Column identifying demographic blocks. It can be the same as `block_id`.
`trial_demo`	Character or character vector identifying the name of the blocks in `demo_id` containing the demographic information.

Value

List of dataframe.

sciat1: Data frame with class sciat_clean containing the data of the first SC-IAT as specified block_sciat_1. If any labels was specified in trial_eliminate, data_keep will contain the already cleaned dataset.
sciat2: Data frame with class sciat_clean containing the data of the second (if any) SC-IAT as specified through block_sciat_2. If any labels was specified in trial_eliminate, data_keep will contain the already cleaned dataset.
data_demo: Data frame. Present only when variable_demo and trial_demo arguments are specified.

Examples

data("raw_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]]
sciat2 <- sciat_data[[2]]
data("raw_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]]
sciat2 <- sciat_data[[2]]

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"))
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")
# 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")

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
)
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)
# 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)

Plot IAT or SC-IAT scores (distribution)

Description

Plot the distribution of the IAT D-score or the SC-IAT D.

Usage

d_density(
  data,
  graph = c("histogram", "density", "violin"),
  n_bin = 80,
  col_fill = "royalblue",
  col_point = "red",
  include_stats = FALSE
)
d_density(
  data,
  graph = c("histogram", "density", "violin"),
  n_bin = 80,
  col_fill = "royalblue",
  col_point = "red",
  include_stats = FALSE
)

Arguments

`data`	Dataframe with either class `dscore` or `dsciat`.
`graph`	String. Indicates the graphs to display. Default is `histogram`
`n_bin`	Numeric. Indicates the number of bins to display.
`col_fill`	String. Indicates the color for filling the bars of the `histogram` or the curve of the `density`. Default is `royalblue`.
`col_point`	String. Indicates the color for the individual scores –each point – in the violin plot. Default is `red`.
`include_stats`	Logical. Indicates whether to add descriptive statistics. The `mean` is depicted with a solid line. The two dashed lines represent +/- 2 s.d. from the mean. Default is `FALSE`.

Value

A ggplot object.

Examples


  # Plotting the IAT D-score
  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")
  d_density(iat_dscore) # Default graph
  d_density(iat_dscore, graph = "histogram",
          n_bin = 30) # Histogram with a different number of bins
  d_density(iat_dscore, graph = "density") # IAT D-score density plot
  d_density(iat_dscore, graph = "violin") # IAT D-score violin plot

  # Plot the SC-IAT D for the first SC-IAT
  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")
  d_density(d_sciat1, graph = "histogram",
          include_stats = TRUE) # SC-IAT D histogram with descriptive
                                   #  statistics
                                   
# Plotting the IAT D-score
  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")
  d_density(iat_dscore) # Default graph
  d_density(iat_dscore, graph = "histogram",
          n_bin = 30) # Histogram with a different number of bins
  d_density(iat_dscore, graph = "density") # IAT D-score density plot
  d_density(iat_dscore, graph = "violin") # IAT D-score violin plot

  # Plot the SC-IAT D for the first SC-IAT
  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")
  d_density(d_sciat1, graph = "histogram",
          include_stats = TRUE) # SC-IAT D histogram with descriptive
                                   #  statistics

Plot either IAT or SC-IAT scores (points)

Description

Plot the individual D-score or SC-IAT D.

Usage

d_point(
  data,
  point_size = 1,
  x_label = "Participant",
  x_values = TRUE,
  order_sbj = c("default", "D-increasing", "D-decreasing"),
  col_point = "springgreen4",
  include_stats = FALSE
)
d_point(
  data,
  point_size = 1,
  x_label = "Participant",
  x_values = TRUE,
  order_sbj = c("default", "D-increasing", "D-decreasing"),
  col_point = "springgreen4",
  include_stats = FALSE
)

Arguments

`data`	Dataframe with either class `dscore` or `dsciat`.
`point_size`	Numeric. Indicates the size of the points in the graph. Default is 1.
`x_label`	Character. Label of the x-axis. Default is `Participant`.
`x_values`	Logical. Shows the values for x-axis (default = `TRUE`).
`order_sbj`	Character. Defines the order with which the participants are displayed. Default is the default order of participants in the dataframe.
`col_point`	Character. Defines the color of the points. Default is `"springgreen4"`.
`include_stats`	Logical. Indicates whether to add descriptive statistics. The `mean` is depicted with a solid line. The two dashed lines represent +/2 s.d. from the mean. Default is `FALSE`.

Value

A ggplot object

Examples


# Plotting the IAT D-score
  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")
  d_point(iat_dscore) # default plot
  d_point(iat_dscore, order_sbj = "D-increasing") # D-score with increasing
                                                 # order
  d_point(iat_dscore, order_sbj = "D-decreasing",
         col_point = "salmon") # D-score with decreasing order changed color
# Plot the SC-IAT D for the first SC-IAT
  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")
  d_point(d_sciat1, col_point = "salmon",
          include_stats = TRUE) # SC-IAT D with descriptive statistics
          
# Plotting the IAT D-score
  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")
  d_point(iat_dscore) # default plot
  d_point(iat_dscore, order_sbj = "D-increasing") # D-score with increasing
                                                 # order
  d_point(iat_dscore, order_sbj = "D-decreasing",
         col_point = "salmon") # D-score with decreasing order changed color
# Plot the SC-IAT D for the first SC-IAT
  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")
  d_point(d_sciat1, col_point = "salmon",
          include_stats = TRUE) # SC-IAT D with descriptive statistics

Descriptive table of either the IAT D-score or the SC-IAT Ds

Description

Descriptive statistics for the IAT D-score or the SC-IAT D.

Usage

descript_d(data, latex = FALSE)
descript_d(data, latex = FALSE)

Arguments

`data`	Dataframe with either class `dscore` or class `dsciat`.
`latex`	Logical. If `TRUE`, the table for the descriptive statistics will be printed in latex format. Default is `FALSE`.

Value

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).

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")
  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
# 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

Data set with SC-IAT D-scores (Dark)

Description

A data set containing the results of the computation of the D-score on the Dark SC-IAT data set. This data set is used for testing the replicability of the results obtained with the compute_sciat() functions.

Usage

data("dsciat1")
data("dsciat1")

Format

A dataframe with 15 variables, as those described in the documentation for the compute_sciat()function.

Data set with SC-IAT D-scores (Milk)

Description

Usage

data("dsciat2")
data("dsciat2")

Format

A dataframe with 15 variables, as those described in the documentation for the compute_sciat() function.

IAT reliability

Description

Compute the practice – test IAT reliability.

Usage

IAT_rel(data)
IAT_rel(data)

Arguments

data

dataframe with class "dscore" (Gawronski et al., 2017).

Value

List of two objects:

Test-practice reliability: contains the IAT reliability.
Number of Participants: Contains the number of participants on which the reliability was computed.

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")
  IAT_rel(iat_dscore)
# 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")
  IAT_rel(iat_dscore)

Data set with IAT D-scores

Description

A data set containing the results for all the possible D-score algorithms for the IAT. All the algorithms are identified by their corresponding label (such as "dscore_d1"). This data set is used for testing the replicability of the results of the compute_iat() function over time.

Usage

data("iatdscores")
data("iatdscores")

Format

A dataframe with 7 variables, the first one contains the respondents' id, the other 6 columns contain a specific D-score algorithm.

Plot SC-IATs scores

Description

Plot the scores from two different SC-IATs.

Usage

multi_dsciat(
  sciat1,
  sciat2,
  graph = c("density", "violin", "point"),
  x_values = TRUE,
  gcolors = c("dark", "greens", "blues", "pinks"),
  label_sc1 = "SC-IAT1",
  label_sc2 = "SC-IAT2",
  label_y = "SC-IAT scores",
  dens_mean = TRUE
)
multi_dsciat(
  sciat1,
  sciat2,
  graph = c("density", "violin", "point"),
  x_values = TRUE,
  gcolors = c("dark", "greens", "blues", "pinks"),
  label_sc1 = "SC-IAT1",
  label_sc2 = "SC-IAT2",
  label_y = "SC-IAT scores",
  dens_mean = TRUE
)

Arguments

`sciat1`	Dataframe with class `dsciat`. Contains the D for the first SC-IAT.
`sciat2`	Dataframe with class `dsciat`. Contains the D for the second SC-IAT.
`graph`	String. Type of graph to display. Default is `density`.
`x_values`	Logical. Shows the values for x-axis (default = `TRUE`). Only for the point graph.
`gcolors`	String. Colors palette for plotting the results. Default is `dark`.
`label_sc1`	String. Label to display in the graph for the first SC-IAT. Default is `SC-IAT1`.
`label_sc2`	String. Label to display in the graph for the first SC-IAT. Default is `SC-IAT2`.
`label_y`	String. Label to plot on the y-axis.
`dens_mean`	Logical. Whether to include the mean in the density plot. Default is TRUE.

Value

A ggplot object

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") # dataframe with the first D
                                            # 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") # dataframe with the first
                                            # D SC-IAT
 multi_dsciat(d_sciat1, d_sciat2) # plot the D of two SC-IATs with default
                                    # settings
# 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") # dataframe with the first D
                                            # 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") # dataframe with the first
                                            # D SC-IAT
 multi_dsciat(d_sciat1, d_sciat2) # plot the D of two SC-IATs with default
                                    # settings

Compute and plot multiple D-scores

Description

Compute and plot multiple D-scores.

Usage

multi_dscore(data, ds = c("built-in", "error-inflation"))
multi_dscore(data, ds = c("built-in", "error-inflation"))

Arguments

`data`	Dataframe of class `iat_clean`.
`ds`	String. Indicates which D-score to compute. `built-in` compute only D-score with the built-in error correction (D1 and D2), `error-inflation` compute the D-scores without built-in correction (D3 to D6).

Value

A list. The first object is a dataframe containing all the computed Dscores. The second object is a ggplot object, depicting the distribution of the D-scores through violin plots.

@import tidyr

Examples


 # Compute multiple IAT D-scores
  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]]

  # compute the mulitple scores and prepare the graphs for the built-in
  # strategies

  multiple_scores <- multi_dscore(iat_data, ds = "built-in")

  data_multiple <- multiple_scores$dscores # store the D-score in a dataframe

  # plot the results
  multiple_scores$graph
  
# Compute multiple IAT D-scores
  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]]

  # compute the mulitple scores and prepare the graphs for the built-in
  # strategies

  multiple_scores <- multi_dscore(iat_data, ds = "built-in")

  data_multiple <- multiple_scores$dscores # store the D-score in a dataframe

  # plot the results
  multiple_scores$graph

Dataset with one IAT and two SC-IATs

Description

A dataset containing the data from 152 participants who completed one IAT and two SC-IATs. The object of both the implicit measures was chocolate, either Milk or Dark chocolate:

Usage

data(raw_data)
data(raw_data)

Format

A dataframe with 6 variables, as follows:

Participant. Participants ID.
latency. Latency of the response times in millisecond.
correct. Response accuracy (0–correct, 1–error).
trialcode. Factor with 32 levels identifying the trial for each response, both for the implicit measures and the demographic questionnaire. It contains also the trials that have to be eliminated, defined as follows:
- alert. Defines the SC-IAT trials beyond the response time window.
- Reminder, Reminder1. Identify the instruction page.
blockcode. Factor with 13 levels as follow:
- practice.iat.Milkbad. IAT practice blocks, Mapping A.
- practice.iat.Milkbad. IAT practice blocks, Mapping B.
- practice.sc_dark.Darkbad. Dark SC-IAT practice blocks, Mapping A.
- practice.sc_dark.Darkbad. Dark SC-IAT practice blocks, Mapping B.
- practice.sc_milk.Milkbad. Milk SC-IAT practice blocks, Mapping A.
- practice.sc_milk.Milkgood. Milk SC-IAT practice blocks, Mapping B.
- test.iat.Milkbad. IAT test blocks, Mapping A.
- test.iat.Milkgood. IAT test blocks, Mapping B.
- test.sc_dark.Darkbad. Dark SC_IAT test blocks, Mapping A.
- test.sc_dark.Darkbad. Dark SC-IAT test blocks, Mapping B.
- test.sc_milk.Milkbad. Milk SC-IAT test blocks, Mapping A.
- test.sc_milk.Milkgood. Milk SC-IAT test blocks, Mapping B.
- demo. Demographic questionnaire.
response. Character registering the type of response for the demographic .

Package 'implicitMeasures'

Help Index

Prepare and clean IAT data.

Description

Usage

Arguments

Value

Examples

Prepare and clean SC-IAT data

Description

Usage

Arguments

Value

Examples

Compute IAT D-score

Description

Usage

Arguments

Value

Examples

Compute the D-score for the SC-IAT

Description

Usage

Arguments

Value

Examples

Plot IAT or SC-IAT scores (distribution)

Description

Usage

Arguments

Value

Examples

Plot either IAT or SC-IAT scores (points)

Description

Usage

Arguments

Value

Examples

Descriptive table of either the IAT D-score or the SC-IAT Ds

Description

Usage

Arguments

Value

Examples

Data set with SC-IAT D-scores (Dark)

Description

Usage

Format

Data set with SC-IAT D-scores (Milk)

Description

Usage

Format

IAT reliability

Description

Usage

Arguments

Value

Examples

Data set with IAT D-scores

Description

Usage

Format

Plot SC-IATs scores

Description

Usage

Arguments

Value

Examples

Compute and plot multiple D-scores

Description

Usage

Arguments

Value

Examples

Dataset with one IAT and two SC-IATs

Description

Usage

Format