splithalf (version 0.2.0)

DPsplithalf: Dot-Probe Split Half

Description

This function calculates split half reliability estimates for Dot Probe data

Usage

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

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

incErrors

include incorrect trials?, defaults to FALSE

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

Value

Returns a data frame containing split-half reliability estimates for the bias index in each condition specified. splithalf returns the raw estimate of the bias index spearmanbrown returns 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.

Examples

## split half estimates for the bias index in two blocks
## using 50 iterations of the random split method (note: 5000 would be standard)
DPsplithalf(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
DPsplithalf(DPdata_missing, conditionlist = c("block1","block2"),
halftype = "random", no.iterations = 50)

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