SC-IAT-example"
In implicitMeasures: Compute Scores for Different Implicit Measures

knitr::opts_chunk$set(
  collapse = TRUE,
#  fig.path = "",
  comment = "#>",
  message = FALSE,
  warning = FALSE
)

library(implicitMeasures)

This vignette illustrates how to use the implicitMeasures package for computing the SC-IAT D score. The illustration is based on the data set included in the package (i.e., raw_data).

First thing first: Import and explore data

The labels that contains the specification sc_ in theblockcode variable identify the SC-IAT blocks.

data("raw_data")
# explore the dataframe
str(raw_data)

# explore the levels of the blockcode variable to identify the SC-IAT blocks
levels(raw_data$blockcode)

raw_data contains data from two different SC-IATs, one for the implicit assessment of the positive/negative evaluation of Milk chocolate (sc_milk), and one for the implicit assessment of the positive/negative evaluation of Dark chocolate (sc_dark).

Once the SC-IATs blocks have been identified, it is possible to clean the data for computing the D score. Function clean_sciat allows for cleaning the data set of either just one SC-IAT or to clean the data sets of two SC-IATs concurrently. The labels identifying the test blocks must be specified as a character vector via argument block_sciat_1 and argument block_sciat_2 (use the block_sciat_2 argument only if there is a second SC-IAT). The labels identifying the demographic information (if any) must be passed to the trial_demo argument, after specifying the column of the data set containing the labels of the demographic information (argument demo_id).

DON'T USE THE trial_eliminate ARGUMENT TO ELIMINATE TRIALS EXCEEDING THE RESPONSE TIME WINDOW (rtw).

The labels for identifying the responses beyond the rtw (that have to be eliminated) must be included in the variable identified by thetrial_id label, but they have to be specified via the non_response argument in the compute_sciat() function to actually be deleted.

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"), 
                         demo_id = "blockcode", 
                         trial_demo = "demo")

Since two SC-IATs and demographic data were specified, clean_sciat() results in a list of 3 elements:

str(sciat_data) # structure of the resulting List

The first two elements (sciat1 and sciat2) are two data.frame with class sciat_clean. They contain the data of the SC-IATs specified in the block_sciat1 and block_sciat2 arguments of the clean_sciat() function, respectively. The third element (demo) is a data.frame that contains the demographic information as specified in the trial_demo argument of function clean_sciat().

Each element of the resulting list can be stored in a separate object.

sciat1 <- sciat_data[[1]] # extract first SC-IAT data
sciat2 <- sciat_data[[2]] # extract second SC-IAT data
demo_data <- sciat_data[[3]] # extract demographic information

head(sciat1)
head(demo_data)

Compute the SC-IAT D-score

Once the SC-IAT(s) data have been cleaned with the clean_sciat() function, it is possible to compute the D score by using function compute_sciat().

This function takes three mandatory arguments and one optional argument. The three mandatory arguments are the data set with class sciat_clean, and the labels identifying the two critical associative conditions (arguments mappingA and mappingB). If the SC-IAT administration included a rtw, the label identifying the trials exceeding the threshold must be specified via the (optional) argument non_response.

# Compute the D-score for the first SC-IAT
 d_sciat1 <- compute_sciat(sciat1,
                  mappingA = "test.sc_dark.Darkbad",
                  mappingB = "test.sc_dark.Darkgood",
                  non_response = "alert")

# dataframe containing the SC-IAT D-score of the of the first SC-IAT
str(d_sciat1) 

# Compute D-score for the second SC-IAT
 d_sciat2 <- compute_sciat(sciat2,
                  mappingA = "test.sc_milk.Milkbad",
                  mappingB = "test.sc_milk.Milkgood",
                  non_response = "alert")

 # dataframe containing the SC-IAT D-score of the of the second SC-IAT
 head(d_sciat2)

The compute_sciat() function results in a data.frame with class dsciat containing a number of rows equal to the number of participants, their D score, and a bunch of useful information on their performance (see the documentation for the compute_sciat() function for further information). The descript_d(), d_point(), and d_density() functions require the object resulting from the compute_sciat() function to work.

Descriptive statistics

The descriptive statistics of the D score and of the response times in the two critical conditions can be easily obtained with the descript_d() function:

descript_d(d_sciat1) # Data frame containing SC-IAT D-scores

By specifying latex = TRUE, the descript_d() function prints the results in LaTeX code

descript_d(d_sciat2, # Data frame containing IAT D-scores
           latex = TRUE) # obtain the code for latex tables

Plotting the results

The implicitMeasures package comes with several functions for obtaining nice and clear representations of the results at both individual respondent and sample levels. Additionally, it includes functions for plotting SC-IAT D scores resulting from two different SC-IATs.

Individual respondent plot

The d_point() function plots the SC-IAT D score for each respondent.

 d_point(d_sciat1) # Data frame containing SC-IAT D-scores

In case of large sample size the label identifying each respondent is not easy to read, and it can be eliminated by setting x_values = FALSE. Respondents can be arranged by increasing or decreasing D scores by setting argument order_sbj equal to "D-increasing" or "D-decreasing", respectively. Descriptive statistics (i.e., $M_{\text{D-score}}\pm 2sd$) can be added by setting include_stats = TRUE. Finally, the color of the points can be changed by using the col_point argument.

```r Function d_point() with settings change"} d_point(d_sciat1, # dataframe containing SC-IAT D-scores order_sbj = "D-increasing", # change respondents' order x_values = FALSE, # remove respondents' labels include_stats = TRUE, # include descriptive statistics col_point = "aquamarine3") # change points color

#### Sample  plot

The `d_density()` function plots the distribution of the SC-IAT *D* scores. It provides different options for choosing the most appropriate representation. 


```r
d_density(d_sciat1) # Data frame containing SC-IAT D-scores

The number of bins can be changed with the n_bin argument. The graph argument can be used for changing the graphical representation of the data. It is possible to choose an histogram representation (graph = "histogram", default), a representation of the density distribution (graph = "density"), or a violin plot (graph = "violin"). The col_fill argument can be used to change the color of the points representing each respondent's score in the violin plot. Finally, also descriptive statistics (i.e., $M_{\text{D-score}} \pm 2sd$) can be added to the graph by setting argument include_stats = TRUE.

```rd_density() function with settings change"} d_density(d_sciat1, # dataframe containing IAT Dscores graph = "density", # change graphical representation include_stats = TRUE) # include descriptive statistics

#### Multiple SC-IATs

The `multi_dsciat()` function plots the distributions of the *D* scores obtained from two different SC-IATs. This function takes only two mandatory arguments, which are the data frames containing the results of each SC-IAT obtained by using function `compute_sciat()`. 
The type of graphical representation can be changed by using `graph`, which is set to `"density" ` by default. Default representation also contains the lines that indicate the mean of each distribution. These lines can be taken out of the graph by setting  `dens_mean = FALSE`. 


```r
multi_dsciat(d_sciat1, # dataframe containing the results of the first SC-IAT
             d_sciat2) # dataframe containing the results of the second SC-IAT

The graph argument can be set equal to "violin" (violin plots of the SC-IATs D scores) or "point" (point representation of both SC-IATs D scores). The labels identifying each SC-IAT can be set with the label_sc1 and label_sc2 arguments (defaults are "SC-IAT 1" and "SC-IAT 2"). The values labels of the $x$-axis can be removed by setting x-values = FALSE (suggested in case of large sample size). The gcolors argument can be used to change the colors of each SC-IAT (default is "dark"). Other colors options are "greens", "blues", and "pink".

multi_dsciat(d_sciat1, # dataframe containing the results of the first SC-IAT
             d_sciat2, # dataframe containing the results of the second SC-IAT
             graph = "point", # change graph type
       x_values = FALSE, # take out x values
       gcolors = "greens", # change color
       label_sc1 = "Dark SC-IAT",  # change label first SC-IAT
       label_sc2 = "Milk SC-IAT") # change label second SC-IAT