R: Reliability and Scoring Routines for the Approach-Avoidance Task

# Reliability and Scoring Routines for the Approach-Avoidance Task

## Documentation for package ‘AATtools’ version 0.0.3

- DESCRIPTION file

## Help Pages

aat_bootstrap | Compute bootstrapped approach-bias scores
aat_compute | Compute simple AAT scores
aat_covreliability | Compute a dataset's reliability from its covariance matrix
aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix
aat_doublemeandiff | AAT score computation algorithms
aat_doublemediandiff | AAT score computation algorithms
aat_dscore | AAT score computation algorithms
aat_dscore_multiblock | AAT score computation algorithms
aat_getstudydata | Simulate AAT datasets and predict parameters
aat_mediandscore | AAT score computation algorithms
aat_regression | AAT score computation algorithms
aat_simulate | Simulate AAT datasets and predict parameters
aat_simulate2 | Simulate AAT datasets and predict parameters
aat_singlemeandiff | AAT score computation algorithms
aat_singlemediandiff | AAT score computation algorithms
aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data
aat_standardregression | AAT score computation algorithms
aat_stimulusscores | Compute stimulus-specific bias scores Computes mean single-difference scores (push - pull) for each stimulus.
aat_stimulus_rest | Compute stimulus-rest correlations of double-difference scores This function provides a statistic that can give an indication of how deviant the responses to specific stimuli are, in comparison to the rest of the stimulus set. The algorithm computes stimulus-rest correlations of stimulus-specific double-difference scores. It takes single-difference approach-avoidance scores for each stimulus, and computes every possible subtraction between individual stimuli from both stimulus categories. It then computes correlations between every such subtraction of stimuli on one hand, and the mean double difference score of all other stimuli. Stimulus-rest correlations are then computed by averaging every such subtraction-rest correlation involving a specific stimulus.
Algorithms | AAT score computation algorithms
calpha | Covariance Matrix-Based Reliability Coefficients
case_prune_3SD | Pre-processing rules
compcorr | Correlation tools
cormean | Compute a minimally biased average of correlation values
correlation-tools | Correlation tools
covEM | Covariance matrix computation with multiple imputation
covrel | Covariance Matrix-Based Reliability Coefficients
erotica | AAT examining approach bias for erotic stimuli
error_prune_dropcases | Pre-processing rules
error_replace_blockmeanplus | Pre-processing rules
FlanaganRulon | Split Half-Based Reliability Coefficients
lambda2 | Covariance Matrix-Based Reliability Coefficients
lambda4 | Covariance Matrix-Based Reliability Coefficients
plot.aat_bootstrap | Compute bootstrapped approach-bias scores
plot.aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix
plot.aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data
plot.aat_stimulus_rest | Compute stimulus-rest correlations of double-difference scores This function provides a statistic that can give an indication of how deviant the responses to specific stimuli are, in comparison to the rest of the stimulus set. The algorithm computes stimulus-rest correlations of stimulus-specific double-difference scores. It takes single-difference approach-avoidance scores for each stimulus, and computes every possible subtraction between individual stimuli from both stimulus categories. It then computes correlations between every such subtraction of stimuli on one hand, and the mean double difference score of all other stimuli. Stimulus-rest correlations are then computed by averaging every such subtraction-rest correlation involving a specific stimulus.
plot.qreliability | Compute psychological experiment reliability
Preprocessing | Pre-processing rules
print.aat_bootstrap | Compute bootstrapped approach-bias scores
print.aat_covreliability | Compute a dataset's reliability from its covariance matrix
print.aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix
print.aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data
print.compcorr | Correlation tools
print.qreliability | Compute psychological experiment reliability
prune_nothing | Pre-processing rules
q_reliability | Compute psychological experiment reliability
q_reliability2 | Compute psychological experiment reliability
r2p | Correlation tools
r2t | Correlation tools
r2z | Correlation tools
RajuCoefficient | Split Half-Based Reliability Coefficients
rconfint | Correlation tools
SpearmanBrown | Split Half-Based Reliability Coefficients
splitrel | Split Half-Based Reliability Coefficients
t2r | Correlation tools
trial_prune_3MAD | Pre-processing rules
trial_prune_3SD | Pre-processing rules
trial_prune_grubbs | Pre-processing rules
trial_prune_percent_sample | Pre-processing rules
trial_prune_percent_subject | Pre-processing rules
trial_prune_SD_dropcases | Pre-processing rules
trial_recode_SD | Pre-processing rules
z2r | Correlation tools