This function calculates split half reliability estimates via a permutation approach for a wide range of tasks. Most of the user inputs relate to the variables in the dataset splithalf needs to read in order to estimate reliability. Currently supports response time and accuracy outcomes, for several scoring methods: average, difference, difference of difference scores, and a DPrime development. The (unofficial) version name is "This function gives me the power to fight like a crow"
splithalf(
data,
outcome = "RT",
score = "difference",
conditionlist = FALSE,
halftype = "random",
permutations = 5000,
var.RT = "latency",
var.ACC = "accuracy",
var.condition = FALSE,
var.participant = "subject",
var.compare = "congruency",
compare1 = "Congruent",
compare2 = "Incongruent",
average = "mean",
plot = FALSE,
round.to = 2,
check = TRUE
)
Returns a data frame containing permutation based split-half reliability estimates
splithalf is the raw estimate of the bias index
spearmanbrown is the spearman-brown corrected estimate of the bias index
Warning: If there are missing data (e.g one condition data missing for one participant) output will include details of the missing data and return a dataframe containing the NA data. Warnings will be displayed in the console.
specifies the raw dataset to be processed
indicates the type of data to be processed, e.g. "RT" or "accuracy"
indicates how the outcome score is calculated, e.g. most commonly the difference score between two trial types. Can be "average", "difference", "difference_of_difference", and "DPrime"
sets conditions/blocks to be processed
specifies the split method; "oddeven", "halfs", or "random"
specifies the number of random splits to run - 5000 is good
specifies the RT variable name in data
specific the accuracy variable name in data
specifies the condition variable name in data - if not specified then splithalf will treat all trials as one condition
specifies the subject variable name in data
specifies the variable that is used to calculate difference scores (e.g. including congruent and incongruent trials)
specifies the first trial type to be compared (e.g. congruent trials)
specifies the second trial type to be compared (e.g. incongruent trials)
use "mean" or "median" to calculate average scores?
logical value giving the option to visualise the estimates in a raincloud plot. defaults to FALSE
sets the number of decimals to round the estimates to defaults to 2
runs several checks of the data to detect participants/conditions/trialtypes with too few trials to run splithalf