IATscores (version 0.2.7)

Pretreatment: Pretreat the IAT data in input.

Description

Convert the initial dataframe of the IAT in a simpler dataframe, which is the input of subsequent functions in this package.

Usage

Pretreatment(IATdata,
  label_subject = "subject",
  label_latency = "latency",
  label_accuracy = "correct",
  label_block = "blockcode",
  block_pair1 = c("pair1_left", "pair1_right"),
  block_pair2 = c("pair2_left", "pair2_right"),
  label_trial = NA,
  trial_left = NA,
  trial_right = NA,
  label_praccrit=NA,
  block_prac=NA,
  block_crit=NA,
  label_stimulus=NA)

Arguments

IATdata

The input dataframe. I consider the the output of the IAT implemented in Inquisit (a row by trial). Only 7 columns are important for computation. - a column with subject numbers - a column with latencies - a column with accuracy (1 = correct, 0 = incorrect) - a column including the block codes, i.e. one or more strings that describe the kind of block (e.g., "compatible" vs. "incompatible") - a column including the trial codes, i.e. one or more strings that describe the kind of trial (e.g., "response_left" vs. "response_right") - a column including information about which are the practice and which the critical combined categorization blocks. - a column with the original stimuli (optional)

label_subject

String. Name of the column in IATdata with the subject numbers

label_latency

String. Name of the column in IATdata with the latencies

label_accuracy

String. Name of the column in IATdata with the accuracy

label_block

String. Name of the column in IATdata with the block names

block_pair1

Vector of strings. Elements of the column indicated in label_block that correspond the one of the critical blocks of the IAT

block_pair2

Vector of strings. Elements of the column indicated in label_block that correspond the the other critical block of the IAT (with respect to the one indicated by block_pair1)

label_trial

String (optional). Name of the column in IATdata with the trial names

trial_left

Vector of strings(optional). Elements of the column indicated in label_trial that correspond to trials that required to to press the left button to give the correct response.

trial_right

Vector of strings(optional). Elements of the column indicated in label_trial that correspond to trials that required to to press the right button to give the correct response.

label_praccrit

String (optional). The column in which the information about practice and critical trials is stored.

block_prac

Vector of strings (optional). The elements of the column indicated in label_praccrit that correspond to the practice combined blocks

block_crit

Vector of strings (optional). The elements of the column indicated in label_praccrit that correspond to the critical combined blocks

label_stimulus

(optional) The variable name in IATdata that keeps information about the stimulus presented in each trial

Value

a dataframe with the following columns:

subject

Univocally identifies a participant.

correct

(logical). has value TRUE or 1 if the trial was answered correctly, FALSE or 0 otherwise.

latency

(numeric). Response latency.

blockcode

(factor). Can assume only two values, "pair1" and "pair2". "pair1" is for one critical block and "pair2" is the other critical block.

praccrit

(factor, optional). Can assume only two values, "prac" is for practice combined categorization block and "crit" is for critical combined categorization block. In a IAT with 60 trials for each double categorization block, the first 20 are sometimes administered as practice block, the other 40 as critical.

trialcode

(factor, optional). Code for the trial, has value "left" if the correct response required to press the left button, "right" if it required to press the right button.

stimulus

(character, optional). The stimulus item.

Examples

# NOT RUN {
#### generate random IAT data ####
set.seed(1234)
rawIATdata <- data.frame(
  # ID of each participant (N = 10)
  ID = rep(1:10, each = 180), 
  # seven-block structure, as in Greenwald, Nosek & Banaji (2003)
  # block 1 = target discrimination (e.g., Bush vs. Gore items)
  # block 2 = attribute discrimination (e.g., Pleasant words vs. unpleasant)
  # block 3 = combined practice (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 4 = combined critical  (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 5 = reversed target discrimination (e.g., Gore vs. Bush)
  # block 6 = reversed combined practice (e.g., Gore + pleasant vs. Bush + unpleasant)
  # block 7 = reversed combined critical (e.g., Gore + pleasant vs. Bush + unpleasant)
  block = rep(c(rep(1:3, each = 20),
                rep(4, 40),
                rep(5:6, each = 20),
                rep(7, 40)), 10),
# expected proportion of errors = 10 percent
  correct = sample(c(0, 1), size = 1800, replace = TRUE, prob = c(.2, .8)),
  # reaction times are generated from a mix of two chi2 distributions,
  # one centered on 550ms and one on 100ms to simulate fast latencies
  latency = round(sample(c(rchisq(1500, df = 1, ncp = 550),
                           rchisq(300, df = 1, ncp = 100)), 1800)))

# add some IAT effect by making trials longer in block 6 and 7
rawIATdata[rawIATdata$block >= 6, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6, "latency"] + 100
  
# add some more effect for subjects 1 to 5
rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] + 100
               
head(rawIATdata)
               
#### pretreat IAT data using function Pretreatment ####
IATdata <- Pretreatment(rawIATdata,
                             label_subject = "ID",
                          label_latency = "latency",
                          label_accuracy = "correct",
                          label_block = "block",
                          block_pair1 = c(3, 4),
                          block_pair2 = c(6, 7),
                          label_praccrit = "block",
                          block_prac = c(3, 6),
                          block_crit = c(4, 7))
# data are now in the correct format
head(IATdata)
# }