Description Usage Arguments Value
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_dscore_multiblock
computes Dscores for pairs of sequential blocks
and averages the resulting score (see Greenwald, Nosek, and Banaji, 2003).
Requires extra blockvar
argument, indicating the name of the block variable.
aat_regression
and aat_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_doublemeanquotient
and aat_doublemedianquotient
compute a logtransformed ratio of approach to avoidance for both stimulus categories and subtract these ratios:
log(mean(pull_target) / mean(push_target))  log(mean(pull_control) / mean(push_control))
aat_singlemeandiff
and aat_singlemediandiff
subtract the mean or median approach reaction time from the mean or median avoidance reaction time.
These algorithms are only sensible if the supplied data contain a single stimulus category.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  aat_doublemeandiff(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_doublemediandiff(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_dscore(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_doublemedianquotient(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_doublemeanquotient(ds, subjvar, pullvar, targetvar, rtvar, ...)
aat_singlemeandiff(ds, subjvar, pullvar, rtvar, ...)
aat_singlemediandiff(ds, subjvar, pullvar, rtvar, ...)

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. 
A data.frame containing participant number and computed AAT score.
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