splithalf (version 0.3.1)

splithalf_ACC: Dot-Probe Split Half

Description

This function calculates split half reliability estimates

Usage

splithalf_ACC(data, RTmintrim = "none", RTmaxtrim = "none",
  conditionlist = FALSE, halftype, no.iterations = 1, var.RT = "latency",
  var.condition = FALSE, var.participant = "subject",
  var.correct = "correct", var.trialnum = "trialnum", removelist = "",
  sdtrim = FALSE)

Arguments

data

specifies the raw dataset to be processed

RTmintrim

specifies the lower cut-off point for RTs

RTmaxtrim

specifies the maximum cut-off point for RTs

conditionlist

sets conditions/blocks to be processed

halftype

specifies the split method; "oddeven", "halfs", or "random"

no.iterations

specifies the number of random splits to run

var.RT

specifies the RT variable name in data

var.condition

specifies the condition variable name in data

var.participant

specifies the subject variable name in data

var.correct

specifies the accuracy variable name in data

var.trialnum

specifies the trial number variable

removelist

specifies a list of participants to be removed

sdtrim

allows the user to trim the data by selected sd (after removal of errors and min/max RTs)

Value

Returns a data frame containing split-half reliability estimates for each condition specified.

splithalf returns the raw estimate

spearmanbrown returns the spearman-brown corrected estimate

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.

Examples

# NOT RUN {
## split half estimates for two blocks of the task
## using 50 iterations of the random split method (note: 5000 would be standard)
splithalf(DPdata, conditionlist = c("block1","block2"), halftype = "random",
no.iterations = 50)
## In datasets with missing data an additional output is generated
## the console will return a list of participants/blocks
## the output will also include a full dataframe of missing values
splithalf(DPdata_missing, conditionlist = c("block1","block2"),
halftype = "random", no.iterations = 50)
# }