aat_splithalf: Compute the bootstrapped split-half reliability for approach-avoidance task data

Description

Compute bootstrapped split-half reliability for approach-avoidance task data.

Usage

aat_splithalf(
  ds,
  subjvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  iters,
  algorithm = c("aat_doublemeandiff", "aat_doublemediandiff", "aat_dscore",
    "aat_dscore_multiblock", "aat_regression", "aat_standardregression",
    "aat_doublemedianquotient", "aat_doublemeanquotient", "aat_singlemeandiff",
    "aat_singlemediandiff"),
  trialdropfunc = c("prune_nothing", "trial_prune_3SD", "trial_prune_SD_dropcases",
    "trial_recode_SD", "trial_prune_percent_subject", "trial_prune_percent_sample"),
  errortrialfunc = c("prune_nothing", "error_replace_blockmeanplus",
    "error_prune_dropcases"),
  casedropfunc = c("prune_nothing", "case_prune_3SD"),
  plot = TRUE,
  include.raw = FALSE,
  parallel = TRUE,
  ...
)
# S3 method for aat_splithalf
print(x, ...)
# S3 method for aat_splithalf
plot(x, type = c("median", "minimum", "maximum", "random"), ...)

Value

A list, containing the mean bootstrapped split-half reliability, bootstrapped 95

a list of data.frames used over each iteration, and a vector containing the split-half reliability of each iteration.

Arguments

ds

a longformat data.frame

subjvar

Quoted name of the participant identifier column

pullvar

Quoted name of the column indicating pull trials. Pull trials should either be represented by 1, or by the second level of a factor.

targetvar

Name of the column indicating trials featuring the target stimulus. Target stimuli should either be represented by 1, or by the second level of a factor.

rtvar

Name of the reaction time column.

iters

Total number of desired iterations. At least 200 are recommended for reasonable confidence intervals; If you want to see plots of your data, 1 iteration is enough.

algorithm

Function (without brackets or quotes) to be used to compute AAT scores. See Algorithms for a list of usable algorithms.

trialdropfunc

Function (without brackets or quotes) to be used to exclude outlying trials in each half. The way you handle outliers for the reliability computation should mimic the way you do it in your regular analyses. It is recommended to exclude outlying trials when computing AAT scores using the mean double-dfference scores and regression scoring approaches, but not when using d-scores or median double-difference scores.

prune_nothing excludes no trials (default)
trial_prune_3SD excludes trials deviating more than 3SD from the mean per participant.
trial_prune_SD_dropcases removes trials deviating more than a specific number of standard deviations from the participant's mean, and removes participants with an excessive percentage of outliers. Required arguments:
- trialsd - trials deviating more than trialsd standard deviations from the participant's mean are excluded (optional; default is 3)
- maxoutliers - participants with a higher percentage of outliers are removed from the data. (optional; default is .15)
trial_recode_SD recodes outlying reaction times to the nearest non-outlying value, with outliers defined as reaction times deviating more than a certain number of standard deviations from the participant's mean. Required argument:
- trialsd - trials deviating more than this many standard deviations from the mean are classified as outliers.
trial_prune_percent_subject and trial_prune_percent_sample remove trials below and/or above certain percentiles, on a subject-by-subject basis or sample-wide, respectively. The following arguments are available:
- lowerpercent and uppperpercent (optional; defaults are .01 and .99).

errortrialfunc

Function (without brackets or quotes) to apply to an error trial.

prune_nothing removes no errors (default).
error_replace_blockmeanplus replaces error trial reaction times with the block mean, plus an arbitrary extra quantity. If used, the following additional arguments are required:
- blockvar - Quoted name of the block variable (mandatory)
- errorvar - Quoted name of the error variable, where errors are 1 or TRUE and correct trials are 0 or FALSE (mandatory)
- errorbonus - Amount to add to the reaction time of error trials. Default is 0.6 (recommended by Greenwald, Nosek, & Banaji, 2003)
error_prune_dropcases removes errors and drops participants if they have more errors than a given percentage. The following arguments are available:
- errorvar - Quoted name of the error variable, where errors are 1 or TRUE and correct trials are 0 or FALSE (mandatory)
- maxerrors - participants with a higher percentage of errors are excluded from the dataset. Default is .15.

casedropfunc

Function (without brackets or quotes) to be used to exclude outlying participant scores in each half. The way you handle outliers here should mimic the way you do it in your regular analyses.

prune_nothing excludes no participants (default)
case_prune_3SD excludes participants deviating more than 3SD from the sample mean.

plot

Create a scatterplot of the AAT scores computed from each half of the data from the last iteration. This is highly recommended, as it helps to identify outliers that can inflate or diminish the reliability.

include.raw

logical indicating whether raw split-half data should be included in the output object.

parallel

If TRUE (default), will use parallel computing to compute results faster. If a doParallel backend has not been registered beforehand, this function will register a cluster and stop it after finishing, which takes some extra time.

...

Other arguments, to be passed on to the algorithm or outlier rejection functions (see arguments above)

x

an aat_splithalf object

type

Character argument indicating which iteration should be chosen. Must be an abbreviation of "median" (default), "minimum", "maximum", or "random".

Author

Sercan Kahveci

Examples

Run this code

split <- aat_splithalf(ds=erotica[erotica$is_irrelevant==0,],
                       subjvar="subject",pullvar="is_pull",targetvar="is_target",
                       rtvar="RT",iters=10,trialdropfunc="trial_prune_3SD",
                       casedropfunc="case_prune_3SD",algorithm="aat_dscore",
                       plot=FALSE,parallel=FALSE)

print(split)
#Mean reliability: 0.521959
#Spearman-Brown-corrected r: 0.6859041
#95%CI: [0.4167018, 0.6172474]

plot(split)

# \donttest{
#Regression Splithalf
aat_splithalf(ds=erotica[erotica$is_irrelevant==0,],
              subjvar="subject", pullvar="is_pull", targetvar="is_target",
              rtvar="RT", iters=10, trialdropfunc="trial_prune_3SD",
              casedropfunc="case_prune_3SD", algorithm="aat_regression",
              formula = RT ~ is_pull * is_target, aatterm = "is_pull:is_target",
              plot=FALSE, parallel=FALSE)
#Mean reliability: 0.5313939
#Spearman-Brown-corrected r: 0.6940003
#95%CI: [0.2687186, 0.6749176]
# }