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

Value

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

Functions

• 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_mediandscore(): computes a double-difference 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 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(): 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_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.

Pre-processing rules

Description

These are pre-processing rules that can be used in aat_splithalf, aat_bootstrap, and aat_compute.

• The following rules are to be used for the trialdropfunc argument. The way you handle outliers for the reliability computation and bootstrapping more broadly 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 double-dfference scores and regression scoring approaches, but not when using d-scores or median double-difference scores.

  • prune_nothing excludes no trials (default)

  • trial_prune_3SD excludes trials deviating more than 3SD from the mean per participant.

  • trial_prune_3MAD excludes trials deviating more than 3 median absolute deviations from the median per participant.

  • trial_prune_grubbs applies a Grubbs' test to the data, removing one outlier at a time until the test is no longer significant.

  • trial_prune_SD_dropcases removes trials deviating more than a specific number of standard deviations from the participant's mean, and removes participants with an excessive percentage of outliers. Required arguments:

    • trialsd - trials deviating more than trialsd standard deviations from the participant's mean are excluded (optional; default is 3)

    • maxoutliers - participants with a higher percentage of outliers are removed from the data. (optional; default is .15)

  • trial_recode_SD recodes outlying reaction times to the nearest non-outlying value, with outliers defined as reaction times deviating more than a certain number of standard deviations from the participant's mean. Required argument:

    • trialsd - trials deviating more than this many standard deviations from the mean are classified as outliers.

  • trial_prune_percent_subject and trial_prune_percent_sample remove trials below and/or above certain percentiles, on a subject-by-subject basis or sample-wide, respectively. The following arguments are available:

    • lowerpercent and uppperpercent (optional; defaults are .01 and .99).

• The following pre-procesing rules are to be used for the errortrialfunc argument. They determine what is to be done with errors - remove or recode?

  • prune_nothing removes no errors (default).

  • error_replace_blockmeanplus replaces error trial reaction times with the block mean, plus an arbitrary extra quantity. If used, the following additional arguments are required:

    • blockvar - Quoted name of the block variable (mandatory)

    • errorvar - Quoted name of the error variable, where errors are 1 or TRUE and correct trials are 0 or FALSE (mandatory)

    • errorbonus - Amount to add to the reaction time of error trials. Default is 0.6 (recommended by Greenwald, Nosek, & Banaji, 2003)

  • error_prune_dropcases removes errors and drops participants if they have more errors than a given percentage. The following arguments are available:

    • errorvar - Quoted name of the error variable, where errors are 1 or TRUE and correct trials are 0 or FALSE (mandatory)

    • maxerrors - participants with a higher percentage of errors are excluded from the dataset. Default is .15.

• These are pre-processing rules to be used for the casedropfunc argument. The way you handle outliers here should mimic the way you do it in your regular analyses.

  • prune_nothing excludes no participants (default)

  • case_prune_3SD excludes participants deviating more than 3SD from the sample mean.

Usage

prune_nothing(ds, ...)

trial_prune_percent_subject(
  ds,
  subjvar,
  rtvar,
  lowerpercent = 0.01,
  upperpercent = 0.99,
  ...
)

trial_prune_percent_sample(
  ds,
  rtvar,
  lowerpercent = 0.01,
  upperpercent = 0.99,
  ...
)

trial_prune_3SD(ds, subjvar, rtvar, ...)

trial_prune_3MAD(ds, subjvar, rtvar, ...)

trial_prune_SD_dropcases(
  ds,
  subjvar,
  rtvar,
  trialsd = 3,
  maxoutliers = 0.15,
  ...
)

trial_recode_SD(ds, subjvar, rtvar, trialsd = 3, ...)

trial_prune_grubbs(ds, subjvar, rtvar, ...)

case_prune_3SD(ds, ...)

error_replace_blockmeanplus(
  ds,
  subjvar,
  rtvar,
  blockvar,
  errorvar,
  errorbonus,
  ...
)

error_prune_dropcases(ds, subjvar, errorvar, maxerrors = 0.15, ...)

Arguments

Compute bootstrapped approach-bias scores

Description

Compute bootstrapped approach-bias scores with confidence intervals.

Usage

aat_bootstrap(
  ds,
  subjvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  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"),
  plot = TRUE,
  include.raw = FALSE,
  parallel = TRUE,
  ...
)

## S3 method for class 'aat_bootstrap'
print(x, ...)

## S3 method for class 'aat_bootstrap'
plot(x, ...)

Arguments

Value

A list, containing bootstrapped bias scores, their variance, bootstrapped 95 percent confidence intervals, the number of iterations, and a matrix of bias scores for each iteration.

Author(s)

Sercan Kahveci

Examples

# Compute 10 bootstrapped AAT scores.
boot<-aat_bootstrap(ds=erotica[erotica$is_irrelevant==0,], subjvar="subject",
                    pullvar="is_pull", targetvar="is_target",rtvar="RT",
                    iters=10,algorithm="aat_doublemediandiff",
                    trialdropfunc="trial_prune_3SD",
                    plot=FALSE, parallel=FALSE)
plot(boot)
print(boot)

Compute simple AAT scores

Description

Compute simple AAT scores, with optional outlier exclusion and error trial recoding.

Usage

aat_compute(
  ds,
  subjvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  algorithm = c("aat_doublemeandiff", "aat_doublemediandiff", "aat_dscore",
    "aat_dscore_multiblock", "aat_regression", "aat_standardregression",
    "aat_doublemeanquotient", "aat_doublemedianquotient", "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"),
  ...
)

Arguments

Examples

#Compute the correlation between relevant-feature and irrelevant-feature AAT scores
ds<-erotica[erotica$correct==1,]
relevant <- aat_compute(ds=ds[ds$is_irrelevant==0,],
                        pullvar="is_pull",targetvar="is_target",
                        rtvar="RT",subjvar="subject",
                        trialdropfunc="trial_prune_3SD",
                        algorithm="aat_doublemediandiff")

irrelevant <- aat_compute(ds=ds[ds$is_irrelevant==1,],
                        pullvar="is_pull",targetvar="is_target",
                        rtvar="RT",subjvar="subject",
                        trialdropfunc="trial_prune_3SD",
                        algorithm="aat_doublemediandiff")

comparison.df <- merge(relevant, irrelevant, by = "subject")
cor(comparison.df$ab.x, comparison.df$ab.y)
# 0.1145726

Compute a dataset's reliability from its covariance matrix

Description

This function computes mean single-difference scores (push minus pull) for individual stimuli, and computes the reliability from that information. Missing values are dealt with using multiple imputation.

This function computes the reliability when stimuli and participants are removed, allowing for the diagnosis of potential sources of unreliability within the data.

Usage

aat_covreliability(
  ds,
  subjvar,
  stimvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  aggfunc = c("mean", "median"),
  algorithm = c("calpha", "lambda2", "lambda4"),
  iters = 5
)

## S3 method for class 'aat_covreliability'
print(x, ...)

aat_covreliability_jackknife(
  ds,
  subjvar,
  stimvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  algorithm = c("calpha", "lambda2", "lambda4"),
  iters = 5,
  holdout = c("both", "participant", "stimulus", "cross")
)

## S3 method for class 'aat_covreliability_jackknife'
print(x, ...)

## S3 method for class 'aat_covreliability_jackknife'
plot(x, ...)

Arguments

Details

When only one stimulus category is indicated, one of the commonly known reliability algorithms provided with the algorithm argument is used. When two stimulus categories are indicated, this function uses Lord's (1963) algorithm to compute the reliability of a double mean difference score, using the algorithms in algorithm to estimate the reliability of indiviau lstimulus categories.

When one wants to compute the reliability of a double median difference score or D-score, aat_splithalf() is recommended instead.

Value

Returns an aat_covreliability object containing the reliability value as well as the dataset and covariance matrix with replaced missing values. When the argument targetvar is provided, the output also contains the reliability of the individual stimulus categories and their intercorrelation.

aat_covreliability_jackknife() returns an aat_covreliability_jackknife object, containing jackknife reliability statistics. If argument holdout was set to "cross", then these statistics are provided in a matrix where rows represent participants and columns represent stimuli. Otherwise, they are provided in data.frames where the stimulus or participant is represented in a column alongside the associated reliability value.

Methods (by generic)

• print(aat_covreliability): Print an aat_covreliability object

Functions

• print(aat_covreliability_jackknife): Print an aat_covreliability_jackknife object

• plot(aat_covreliability_jackknife): Plot an aat_covreliability_jackknife object

References

Lord, F.Y. (1963), "Elementary Models for Measuring Change", in Problems in Measuring Change, C.W. Harris, ed.. Madison. Wisconsin: University of Wisconsin.

Examples

#We generate a dataset with 16 stimuli in each category
ds<-aat_simulate(biasfx_jitter=40,nstims=16)
ds$stim<-paste0(ds$stim,"-",ds$is_target)

# If Lord's formula and
# bootstrapped splithalf measure something similar,
# then the outcomes should be close to each other.
aat_covreliability(ds=ds,subjvar="subj",stimvar="stim",pullvar="is_pull",
                           targetvar="is_target",rtvar="rt")
aat_splithalf(ds=ds,subjvar="subj",pullvar="is_pull",targetvar="is_target",rtvar="rt",
              algorithm="aat_doublemeandiff",iters=100,plot=FALSE)

#Testing reliability for single-difference scores
ds<-ds[ds$is_target==1,]
aat_covreliability(ds=ds,subjvar="subj",stimvar="stim",pullvar="is_pull",rtvar="rt")
hh<-aat_simulate()
test<-aat_covreliability_jackknife(ds=hh,subjvar="subj",stimvar="stim",pullvar="is_pull",
                                   targetvar="is_target",rtvar="rt",holdout="cross")
print(test)
plot(test)

Simulate AAT datasets and predict parameters

Description

aat_simulate() generates approach-avoidance task datasets.

aat_getstudydata() retrieves the properties of datasets from a number of pre-existing studies

Usage

aat_simulate(
  npps = 36,
  nstims = 16,
  stimreps = 4,
  meanrt = 632,
  meanrt_jitter = 90.1,
  sdrt = 158,
  sdrt_jitter = 49.9,
  pullfx = -39.2,
  pullfx_jitter = 40.5,
  stimfx = -30.9,
  stimfx_jitter = 32.5,
  biasfx = 39,
  biasfx_jitter = 60.1,
  empirical = FALSE,
  ...
)

aat_simulate2(..., defaults = "none", slowols = 0, fastols = 0, olsd = 3)

aat_getstudydata()

Arguments

Details

Defaults of aat_simulate() are based on Kahveci, Van Alebeek, Berking, & Blechert (2021).

"Lender2018" parameters are taken from the relevant-feature AAT of Lender, Meule, Rinck, Brockmeyer, & Blechert (2018). "Kahveci2021" parameters are taken from Kahveci, Van Alebeek, Berking, & Blechert (in review).

Lender, A., Meule, A., Rinck, M., Brockmeyer, T., & Blechert, J. (2018). Measurement of food-related approach–avoidance biases: Larger biases when food stimuli are task relevant. Appetite, 125, 42-47.

Kahveci, S., Van Alebeek, H., Berking, M., & Blechert, J. (in review). Touchscreen based assessment of food approach biases: investigation of reliability and stimulus-specific effects.

Value

aat_simulate() returns a data.frame with the following columns: subj (participant ID), stim (stimulus number), rep (stimulus repetition number), is_pull (0 = avoid, 1 = approach), is_target (0 = control stimulus, 1 = target stimulus), meanrt (participant's mean RT), sdrt (participant's residual standard deviation), pullfx (participant approach-avoidance effect size in ms), stimfx (participant stimulus category effect size in ms), biasfx (participant approach bias effect size in ms), and rt (trial reaction time). Additionally, the data.frame has the attribute population_reliability which represents the expected reliability of the data given the provided parameters.

Examples

ts<- aat_simulate(pullfx = 50, stimfx = 10, biasfx = 100)
mod<-lm(rt~is_pull*is_target,data=ts)
coef(mod) #these should be somewhat close to the provided coefficients

# Here's how one might derive the parameters used in this function from a real dataset
## Not run: 
mod<-lmer(decisiontime ~ is_pull * is_food + (is_pull * is_food | subjectid),data=dsa)
fixef(mod) # from here, all the fx and mean RTs are derived
ranef(mod)$subjectid %>% apply(2,sd) #from here, all the fx jitters are derived
dsa %>% group_by(subjectid) %>% summarise(sd=sd(resid)) %>%
summarise(m=mean(sd),s=sd(sd)) # from here, sdrt_jitter is derived

## End(Not run)
hist(aat_simulate2(defaults="Lender2018_relevant_raw",slowols=10,fastols=10)$rt)

Compute the bootstrapped split-half reliability for approach-avoidance task data

Description

Compute bootstrapped split-half reliability for approach-avoidance task data.

Usage

aat_splithalf(
  ds,
  subjvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  stratvars = NULL,
  iters = 6000,
  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

Details

The calculated split-half coefficients are described in Warrens (2016).

Value

A list, containing the mean bootstrapped split-half reliability, bootstrapped 95 a list of data.frames used over each iteration, and a vector containing the split-half reliability of each iteration.

Note

The Spearman-Brown, Raju, and Flanagan-Rulon correlation coefficients are modified to prevent negative correlations from reaching extreme and impossible values (e.g. below -1).

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), 71-84.

See Also

q_reliability

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)

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)

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.

Description

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.

Usage

aat_stimulus_rest(
  ds,
  subjvar,
  stimvar,
  pullvar,
  targetvar,
  rtvar,
  method = c("pearson", "spearman", "kendall")
)

## S3 method for class 'aat_stimulus_rest'
plot(x, ...)

Arguments

Value

Returns a aat_stimulus_rest object containing statistics for each stimulus. Stats include the average stimulus-rest correlation (mcor); the standard deviation of dyad-rest correlations for this stimulus (sdcor); the number of valid correlations involved in these statistic (n); the average percentile of dyad-rest correlations involving the stimulus within the distribution of all other dyad-rest correlations (restpercentile); as well as z-scores (zpercentile) and p-values for this percentile (pval).

Examples

ds<-aat_simulate()
stimrest<-aat_stimulus_rest(ds,subjvar="subj",stimvar="stim",pullvar="is_pull",
                     targetvar="is_target",rtvar="rt")
plot(stimrest)
print(stimrest)

Compute stimulus-specific bias scores Computes mean single-difference scores (push - pull) for each stimulus.

Description

Compute stimulus-specific bias scores Computes mean single-difference scores (push - pull) for each stimulus.

Usage

aat_stimulusscores(
  ds,
  subjvar,
  stimvar,
  pullvar,
  targetvar = NULL,
  rtvar,
  aggfunc = c("mean", "median"),
  iters = 5
)

Arguments

Value

Exports a list containing a data.frame with stimulus-specific bias scores, indicated in the column names, a covariance matrix of that same data, and a data.frame indicating to which stimulus category each stimulus belongs.

Examples

ds<-aat_simulate(biasfx_jitter=40,nstims=16)
ds$stim<-paste0(ds$stim,"-",ds$is_target)
aat_stimulusscores(ds,"subj","stim","is_pull","is_target","rt")

Compute a minimally biased average of correlation values

Description

This function computes a minimally biased average of correlation values. This is needed because simple averaging of correlations is negatively biased, and the often used z-transformation method of averaging correlations is positively biased. The algorithm was developed by Olkin & Pratt (1958).

Usage

cormean(
  r,
  n,
  wts = c("none", "n", "df"),
  type = c("OP5", "OP2", "OPK"),
  na.rm = F
)

Arguments

Value

An average correlation.

References

Olkin, I., & Pratt, J. (1958). Unbiased estimation of certain correlation coefficients. The Annals of Mathematical Statistics, 29. https://doi.org/10.1214/aoms/1177706717

Shieh, G. (2010). Estimation of the simple correlation coefficient. Behavior Research Methods, 42(4), 906-917. https://doi.org/10.3758/BRM.42.4.906

Examples

cormean(c(0,.3,.5),c(30,30,60))

Correlation tools

Description

Helper functions to compute important statistics from correlation coefficients.

Usage

r2z(r)

z2r(z)

r2t(r, n)

t2r(t, n)

r2p(r, n)

rconfint(r, n, alpha = 0.05)

compcorr(r1, r2, n1, n2)

## S3 method for class 'compcorr'
print(x, ...)

Arguments

Functions

• r2z(): converts correlation coefficients to z-scores

• z2r(): converts z-scores to correlation coefficients

• r2t(): Converts correlation coefficients to t-scores

• t2r(): Converts t-scores to correlation coefficients

• r2p(): Computes the two-sided p-value for a given correlation

• rconfint(): Computes confidence intervals for one or multiple correlation coefficients

• compcorr(): computes the significance of the difference between two correlation coefficients

• print(compcorr): computes the significance of the difference between two correlation coefficients

See Also

cormean

Examples

z <- r2z(.5)
r <- z2r(z)
t<-r2t(r,30)
r2p(r,30)
print(rconfint(r,30))
print(compcorr(.5,.7,20,20))

Covariance matrix computation with multiple imputation

Description

This function computes a covariance matrix from data with some values missing at random. The code was written by Eric from StackExchange. https://stats.stackexchange.com/questions/182718/ml-covariance-estimation-from-expectation-maximization-with-missing-data

Usage

covEM(dat_missing, iters = 1000)

Arguments

References

Beale, E. M. L., & Little, R. J. A.. (1975). Missing Values in Multivariate Analysis. Journal of the Royal Statistical Society. Series B (methodological), 37(1), 129–145.

Examples

# make data with missing values
missing_mtcars <- mtcars
for(i in 1:20){
  missing_mtcars[sample(1:nrow(mtcars),1),sample(1:ncol(mtcars),1)]<-NA
}
covmat<-covEM(as.matrix(missing_mtcars))$sigma
calpha(covmat)

Covariance Matrix-Based Reliability Coefficients

Description

These functions allow for the computation of the reliability of a dataset from the covariance matrix of its variables.

Usage

calpha(covmat)

lambda2(covmat)

lambda4(covmat)

Arguments

Functions

• calpha(): Cronbach's alpha

• lambda2(): Guttman's Lambda-2

• lambda4(): Guttman's Lambda-4. This algorithm tries to get the highest attainable reliability by

See Also

splitrel

Examples

# compute reliability from covariance
h<-cov(iris[,1:4])
calpha(h)
lambda2(h)
lambda4(h)
# Lambda-2 and Lambda-4 are significantly larger because
# some of the variables in the iris dataset are negatively correlated.

AAT examining approach bias for erotic stimuli

Description

AAT

Usage

erotica

Format

An object of class "data.frame"

Source

osf.io repository

References

Kahveci, S., Van Bockstaele, B.D., & Wiers, R.W. (in preparation). Pulling for Pleasure? Erotic Approach-Bias Associated With Porn Use, Not Problems. DOI:10.17605/OSF.IO/6H2RJ

Compute psychological experiment reliability

Description

This function can be used to compute an exact reliability score for a psychological task whose results involve a difference score. The resulting intraclass correlation coefficient is equivalent to the average all possible split-half reliability scores. It ranges from -1 to 1, with -1 implying that all variance in the data is explained by within-subjects variability, 1 implying that all variance is explained by between-subjects variability, and 0 implying that within-subjects and between-subjects variability contribute equally to the total variance in the sample.

Usage

q_reliability(ds, subjvar, formula, aatterm = NA)

q_reliability2(ds, subjvar, splitvars, rtvar, dscore = F, na.rm = F)

## S3 method for class 'qreliability'
print(x, ...)

## S3 method for class 'qreliability'
plot(x, ...)

Arguments

Value

a qreliability object, containing the reliability coefficient, and a data.frame with participants' bias scores and score variance.

Please note that the valence of the bias scores may or may not correspond with approach and avoidance. If you plan to use these scores in your analyses, always verify that they are in the right direction by correlating them with independently calculated bias scores, for example using aat_compute().

Author(s)

Sercan Kahveci

Examples

# Double-difference score reliability
q_reliability(ds=erotica,subjvar="subject",
              formula= RT ~ is_pull * is_target, aatterm = "is_pull:is_target")

# Single-difference reliability for target stimuli
q_reliability(ds=erotica[erotica$is_target ==1,],subjvar="subject",
              formula= RT ~ is_pull, aatterm = "is_pull")

# Reliability of the mean reaction time of approaching target stimuli (no difference score)
q_reliability(ds=erotica[erotica$is_target ==1 & erotica$is_pull ==1,],subjvar="subject",
              formula= RT ~ 1, aatterm = "1")

q_reliability2(ds=erotica,subjvar="subject",
              splitvars=c("is_pull", "is_target"),rtvar="RT")

Split Half-Based Reliability Coefficients

Description

Split Half-Based Reliability Coefficients

Usage

SpearmanBrown(corr, ntests = 2, fix.negative = c("none", "nullify", "mirror"))

FlanaganRulon(x1, x2, fix.negative = c("none", "nullify", "mirror"))

RajuCoefficient(x1, x2, prop, fix.negative = c("none", "nullify", "mirror"))

Arguments

Value

Spearman-Brown-corrected correlation coefficient.

Functions

• SpearmanBrown(): Perform a Spearman-Brown correction on the provided correlation score.

• FlanaganRulon(): Compute the true reliability using the Flanagan-Rulon formula, which takes into account inequal variances between split halves.

• RajuCoefficient(): Compute split-half reliability using the Raju formula, which takes into account unequal split-halves and variances.

See Also

covrel

Examples

SpearmanBrown(.5)
FlanaganRulon(a<-rnorm(50),rnorm(50)+a*.5,fix.negative="mirror")
a<-rnorm(50)
b<-rnorm(50)+a*.5
RajuCoefficient(a,b,prop=.4,fix.negative="mirror")