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).
The labels that contains the specification sc_ in
theblockcode variable identify the SC-IAT blocks.
data("raw_data")
# explore the dataframe
str(raw_data)
#> 'data.frame': 84726 obs. of 6 variables:
#> $ Participant: int 4 4 4 4 4 4 4 4 4 4 ...
#> $ latency : int 2592 628 808 783 2059 1114 608 663 771 676 ...
#> $ correct : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ trialcode : Factor w/ 32 levels "age","alert",..: 31 5 3 20 3 20 5 3 20 3 ...
#> $ blockcode : Factor w/ 13 levels "demo","practice.iat.Milkbad",..: 4 4 4 4 4 4 4 4 4 4 ...
#> $ response : Factor w/ 46 levels "","0","1","19",..: 43 43 43 43 43 43 43 43 43 43 ...
# explore the levels of the blockcode variable to identify the SC-IAT blocks
levels(raw_data$blockcode)
#> [1] "demo" "practice.iat.Milkbad"
#> [3] "practice.iat.Milkgood" "practice.sc_dark.Darkbad"
#> [5] "practice.sc_dark.Darkgood" "practice.sc_milk.Milkbad"
#> [7] "practice.sc_milk.Milkgood" "test.iat.Milkbad"
#> [9] "test.iat.Milkgood" "test.sc_dark.Darkbad"
#> [11] "test.sc_dark.Darkgood" "test.sc_milk.Milkbad"
#> [13] "test.sc_milk.Milkgood"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
#> List of 3
#> $ sciat1:Classes 'sciat_clean' and 'data.frame': 23328 obs. of 5 variables:
#> ..$ participant: int [1:23328] 4 4 4 4 4 4 4 4 4 4 ...
#> ..$ block : chr [1:23328] "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" ...
#> ..$ trial : Factor w/ 32 levels "age","alert",..: 5 3 5 3 20 3 5 20 5 5 ...
#> ..$ correct : int [1:23328] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ latency : int [1:23328] 461 1495 639 573 671 643 714 1190 625 517 ...
#> $ sciat2:Classes 'sciat_clean' and 'data.frame': 23328 obs. of 5 variables:
#> ..$ participant: int [1:23328] 4 4 4 4 4 4 4 4 4 4 ...
#> ..$ block : chr [1:23328] "test.sc_milk.Milkbad" "test.sc_milk.Milkbad" "test.sc_milk.Milkbad" "test.sc_milk.Milkbad" ...
#> ..$ trial : Factor w/ 32 levels "age","alert",..: 3 23 3 3 3 23 23 23 20 20 ...
#> ..$ correct : int [1:23328] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ latency : int [1:23328] 653 990 594 550 818 591 570 620 661 623 ...
#> $ demo :'data.frame': 3726 obs. of 6 variables:
#> ..$ participant: int [1:3726] 4 4 4 4 4 4 4 4 4 4 ...
#> ..$ latency : int [1:3726] 53047 53047 53047 53047 21554 21554 11266 11266 11266 11266 ...
#> ..$ correct : int [1:3726] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ trialcode : Factor w/ 32 levels "age","alert",..: 19 1 21 8 22 4 10 11 12 13 ...
#> ..$ blockcode : Factor w/ 13 levels "demo","practice.iat.Milkbad",..: 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ response : Factor w/ 46 levels "","0","1","19",..: 33 15 41 34 16 20 1 1 1 1 ...
#> - attr(*, "class")= chr [1:2] "sciat_clean" "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)
#> participant block trial correct latency
#> 23 4 test.sc_dark.Darkbad darkleft 1 461
#> 24 4 test.sc_dark.Darkbad badleft 1 1495
#> 25 4 test.sc_dark.Darkbad darkleft 1 639
#> 26 4 test.sc_dark.Darkbad badleft 1 573
#> 27 4 test.sc_dark.Darkbad goodright 1 671
#> 28 4 test.sc_dark.Darkbad badleft 1 643
head(demo_data)
#> participant latency correct trialcode blockcode response
#> 81001 4 53047 1 gender demo male
#> 81002 4 53047 1 age demo 29
#> 81003 4 53047 1 job demo stud
#> 81004 4 53047 1 edu demo MD
#> 81005 4 21554 1 milk_eval demo 3
#> 81006 4 21554 1 dark_eval demo 4Once 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)
#> Classes 'dsciat' and 'data.frame': 162 obs. of 15 variables:
#> $ participant : chr "100" "101" "102" "103" ...
#> $ n_trial : int 144 144 144 144 144 144 144 144 144 144 ...
#> $ no_response : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ nslow10000 : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ nfast400 : num 0.03 0.05 0.01 0.01 0 0 0.01 0.11 0.02 0.05 ...
#> $ nfast350 : num 0.01 0.01 0 0 0 0 0 0.01 0 0 ...
#> $ out_accuracy : chr "keep" "keep" "keep" "keep" ...
#> $ accuracy.mappingA: num 0.986 0.986 0.958 0.986 1 ...
#> $ accuracy.mappingB: num 0.944 0.986 0.931 0.986 0.931 ...
#> $ RT_mean.mappingA : num 626 785 627 586 885 ...
#> $ RT_mean.mappingB : num 730 913 704 633 1056 ...
#> $ cond_ord : chr "MappingB_First" "MappingB_First" "MappingB_First" "MappingB_First" ...
#> $ legendMappingA : chr "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" "test.sc_dark.Darkbad" ...
#> $ legendMappingB : chr "test.sc_dark.Darkgood" "test.sc_dark.Darkgood" "test.sc_dark.Darkgood" "test.sc_dark.Darkgood" ...
#> $ d_sciat : num -0.413 -0.242 -0.393 -0.288 -0.314 ...
# 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)
#> participant n_trial no_response nslow10000 nfast400 nfast350 out_accuracy
#> 1 100 144 0 0 0.02 0.01 keep
#> 2 101 144 0 0 0.02 0.00 keep
#> 3 102 144 0 0 0.02 0.00 keep
#> 4 103 144 0 0 0.00 0.00 keep
#> 5 104 144 0 0 0.00 0.00 keep
#> 6 105 144 0 0 0.03 0.01 keep
#> accuracy.mappingA accuracy.mappingB RT_mean.mappingA RT_mean.mappingB
#> 1 0.9436620 0.9722222 695.2269 648.8692
#> 2 0.9861111 1.0000000 917.3706 699.7917
#> 3 0.9583333 0.9166667 757.4988 784.3854
#> 4 0.9861111 0.9722222 640.5077 607.7566
#> 5 1.0000000 0.9861111 849.7500 928.0496
#> 6 1.0000000 1.0000000 540.3099 614.8611
#> cond_ord legendMappingA legendMappingB d_sciat
#> 1 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood 0.16120777
#> 2 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood 0.58453600
#> 3 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood -0.08226841
#> 4 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood 0.24119095
#> 5 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood -0.26484892
#> 6 MappingB_First test.sc_milk.Milkbad test.sc_milk.Milkgood -0.50147795The 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.
The computation of the SC-IAT D is such that positive scores indicate slower response times in the condition labeled as mapping A.
The summary method can be applied on the SC-IAT results as well.
summary(d_sciat1) # Data frame containing SC-IAT D scores
#> $general
#> measure value
#> 1 Participants 162
#> 2 Total trials 23324
#>
#> $statistics
#> measure mean sd
#> 1 Response time Mapping A 700.53321700 125.93988156
#> 2 Response time Mapping B 710.22568799 125.28378587
#> 3 Accuracy Mapping A 0.94935298 0.06674879
#> 4 Accuracy Mapping B 0.94800255 0.06494543
#> 5 SC-IAT D-score -0.04118897 0.32127650
#>
#> attr(,"class")
#> [1] "summary_dsciat" "list"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.
The plot method can be used as well on the SC-IAT results.
