splithalf (version 0.3.1)

splithalf_diff_diff: Split Half for difference scores of difference scores

Description

This function calculates split half reliability estimates for Dot Probe data

Usage

splithalf_diff_diff(data, RTmintrim = "none", RTmaxtrim = "none",
  incErrors = FALSE, condition1 = "Assessment1",
  condition2 = "Assessment2", halftype = "random", no.iterations = 5000,
  var.RT = "latency", var.condition = FALSE, var.participant = "subject",
  var.correct = "correct", var.trialnum = "trialnum",
  var.compare = "congruency", compare1 = "Congruent",
  compare2 = "Incongruent", removelist = "", average = "mean",
  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

incErrors

include incorrect trials?, defaults to FALSE

condition1

specifies the first condition

condition2

specifies the second condition

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 - if not specified then splithalf will treat all trials as one condition

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

var.compare

specified the variable that is used to calculate difference scores (e.g. including congruent and incongruent trials)

compare1

specifies the first trial type to be compared (e.g. congruent trials)

compare2

specifies the first trial type to be compared (e.g. incongruent trials)

removelist

specifies a list of participants to be removed

average

allows the user to specify whether mean or median will be used to create the bias index

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 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

# NOT RUN {
## split half estimates for the bias index in two blocks
## using 50 iterations of the random split method (note: 5000 would be standard)
# not run:
# splithalf_diff(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
# not run:
# splithalf_diff(DPdata_missing, conditionlist = c("block1","block2"),
# halftype = "random", no.iterations = 50)
# }