aat_splithalf {AATtools}  R Documentation 
Compute the bootstrapped splithalf reliability for approachavoidance task data
Description
Compute bootstrapped splithalf reliability for approachavoidance task data.
Usage
aat_splithalf(
ds,
subjvar,
pullvar,
targetvar = NULL,
rtvar,
stratvars = NULL,
iters,
algorithm = c("aat_doublemeandiff", "aat_doublemediandiff", "aat_dscore",
"aat_dscore_multiblock", "aat_regression", "aat_standardregression",
"aat_singlemeandiff", "aat_singlemediandiff"),
trialdropfunc = c("prune_nothing", "trial_prune_3SD", "trial_prune_3MAD",
"trial_prune_SD_dropcases", "trial_recode_SD", "trial_prune_percent_subject",
"trial_prune_percent_sample", "trial_prune_grubbs"),
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 class 'aat_splithalf'
print(x, coef = c("SpearmanBrown", "Raju", "FlanaganRulon"), ...)
## S3 method for class 'aat_splithalf'
plot(x, type = c("median", "minimum", "maximum", "random"), ...)
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. 
stratvars 
Names of additional variables to stratify splits by. 
iters 
Total number of desired iterations. At least 6000 are recommended for reasonable estimates. 
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 doubledfference scores and regression scoring approaches, but not when using dscores or median doubledifference scores.

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

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.

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 splithalf 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 
coef 
Optional character argument, indicating which reliability coefficient should be printed. Defaults to Raju's beta. 
type 
Character argument indicating which iteration should be chosen. Must be an abbreviation of

Details
The calculated splithalf coefficients are described in Warrens (2016).
Value
A list, containing the mean bootstrapped splithalf reliability, bootstrapped 95 a list of data.frames used over each iteration, and a vector containing the splithalf reliability of each iteration.
Author(s)
Sercan Kahveci
References
Warrens, M. J. (2016). A comparison of reliability coefficients for psychometric tests that consist of two parts. Advances in Data Analysis and Classification, 10(1), 7184.
See Also
Examples
split < aat_splithalf(ds=erotica[erotica$is_irrelevant==0,],
subjvar="subject", pullvar="is_pull", targetvar="is_target",
rtvar="RT", stratvars="stimuluscode", iters=10,
trialdropfunc="trial_prune_3SD",
casedropfunc="case_prune_3SD", algorithm="aat_dscore",
plot=FALSE, parallel=FALSE)
print(split)
#Mean reliability: 0.521959
#SpearmanBrowncorrected r: 0.6859041
#95%CI: [0.4167018, 0.6172474]
plot(split)
#Regression Splithalf
aat_splithalf(ds=erotica[erotica$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
#SpearmanBrowncorrected r: 0.6940003
#95%CI: [0.2687186, 0.6749176]