aat_doublemeandiff computes a mean-based double-difference score:
(mean(push_target) - mean(pull_target)) - (mean(push_control) - mean(pull_control))
aat_doublemediandiff computes a median-based double-difference score:
(median(push_target) - median(pull_target)) - (median(push_control) - median(pull_control))
aat_dscore computes D-scores for a 2-block 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 D-scores 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 t-score 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 log-transformed 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.
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, ...)
A data.frame containing participant number and computed AAT score.
A long-format data.frame
Column name of the participant identifier variable
Column name of the movement variable (0: avoid; 1: approach)
Column name of the stimulus category variable (0: control stimulus; 1: target stimulus)
Column name of the reaction time variable
Other arguments passed on by functions (ignored)
name of the variable indicating block number
A regression formula to fit to the data to compute an AAT score
A character naming the formula term representing the approach bias. Usually this is the interaction of the movement-direction and stimulus-category terms.