Algorithms {AATtools}  R Documentation 
AAT score computation algorithms
Description
AAT score computation algorithms
Usage
aat_doublemeandiff(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_doublemediandiff(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_dscore(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_mediandscore(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_dscore_multiblock(ds, subjvar, pullvar, targetvar, rtvar, blockvar, ...)
aat_regression(ds, subjvar, formula, aatterm, ...)
aat_standardregression(ds, subjvar, formula, aatterm, ...)
aat_singlemeandiff(ds, subjvar, pullvar, rtvar, ...)
aat_singlemediandiff(ds, subjvar, pullvar, rtvar, ...)
Arguments
ds 
A longformat data.frame 
subjvar 
Column name of the participant identifier variable 
pullvar 
Column name of the movement variable (0: avoid; 1: approach) 
targetvar 
Column name of the stimulus category variable (0: control stimulus; 1: target stimulus) 
rtvar 
Column name of the reaction time variable 
... 
Other arguments passed on by functions (ignored) 
blockvar 
name of the variable indicating block number 
formula 
A regression formula to fit to the data to compute an AAT score 
aatterm 
A character naming the formula term representing the approach bias. Usually this is the interaction of the movementdirection and stimuluscategory terms. 
Value
A data.frame containing participant number and computed AAT score.
Functions

aat_doublemeandiff()
: computes a meanbased doubledifference score:(mean(push_target)  mean(pull_target))  (mean(push_control)  mean(pull_control))

aat_doublemediandiff()
: computes a medianbased doubledifference score:(median(push_target)  median(pull_target))  (median(push_control)  median(pull_control))

aat_dscore()
: computes Dscores for a 2block design (see Greenwald, Nosek, and Banaji, 2003):((mean(push_target)  mean(pull_target))  (mean(push_control)  mean(pull_control))) / sd(participant_reaction_times)

aat_mediandscore()
: computes a doubledifference score usign medians, and divides it by the median absolute deviation of the participant's overall reaction times:((median(push_target)  median(pull_target))  (median(push_control)  median(pull_control))) / mad(participant_reaction_times)

aat_dscore_multiblock()
: computes Dscores for pairs of sequential blocks and averages the resulting score (see Greenwald, Nosek, and Banaji, 2003). Requires extrablockvar
argument, indicating the name of the block variable. 
aat_regression()
:aat_regression
andaat_standardregression
fit regression models to participants' reaction times and extract a term that serves as AAT score.aat_regression
extracts the raw coefficient, equivalent to a mean difference score.aat_standardregression
extracts the tscore of the coefficient, standardized on the basis of the variability of the participant's reaction times. These algorithms can be used to regress nuisance variables out of the data before computing AAT scores. When using these functions, additional arguments must be provided:
formula
 a formula to fit to the data 
aatterm
 the term within the formula that indicates the approach bias; this is usually the interaction of the pull and target terms.


aat_standardregression()
: See above 
aat_singlemeandiff()
: subtracts the mean approach reaction time from the mean avoidance reaction time. Using this algorithm is only sensible if the supplied data contain a single stimulus category. 
aat_singlemediandiff()
: subtracts the median approach reaction time from the median avoidance reaction time. Using this algorithm is only sensible if the supplied data contain a single stimulus category.