SplitHalf: Split half reliability

In IATscores: Implicit Association Test Scores Using Robust Statistics

Description

Compute split half reliability for the algorithms defined by all the combinations of parameters P1, P2, P3, and P4.

Usage

SplitHalf(IATdata, ...)
SplitHalf.D2(IATdata, ...)
SplitHalf.D5(IATdata, ...)
SplitHalf.D6(IATdata, ...)
SplitHalf.D2SWND(IATdata, ...)
SplitHalf.D5SWND(IATdata, ...)
SplitHalf.D6SWND(IATdata, ...)

Arguments

IATdata	same as RobustScores
...	other parameters to be passed to RobustScores

Details

The split-half reliability is computed by splitting the dataframe IATdata in two halves and then calling function RobustScores Functions SplitHalf.D2 etc. are wrappers that allow computing reliability for some common types of scores. See RobustScores.

Value

A vector of split-half reliabilities.

Author(s)

Giulio Costantini

See Also

RobustScores, alg2param

Examples

#### generate random IAT data ####
set.seed(1234)
rawIATdata <- data.frame(
  # ID of each participant (N = 10)
  ID = rep(1:10, each = 180), 
  # seven-block structure, as in Greenwald, Nosek & Banaji (2003)
  # block 1 = target discrimination (e.g., Bush vs. Gore items)
  # block 2 = attribute discrimination (e.g., Pleasant words vs. unpleasant)
  # block 3 = combined practice (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 4 = combined critical  (e.g., Bush + pleasant vs. Gore + unpleasant)
  # block 5 = reversed target discrimination (e.g., Gore vs. Bush)
  # block 6 = reversed combined practice (e.g., Gore + pleasant vs. Bush + unpleasant)
  # block 7 = reversed combined critical (e.g., Gore + pleasant vs. Bush + unpleasant)
  block = rep(c(rep(1:3, each = 20),
                rep(4, 40),
                rep(5:6, each = 20),
                rep(7, 40)), 10),
 # expected proportion of errors = 10 percent
  correct = sample(c(0, 1), size = 1800, replace = TRUE, prob = c(.2, .8)),
  # reaction times are generated from a mix of two chi2 distributions,
  # one centered on 550ms and one on 100ms to simulate fast latencies
  latency = round(sample(c(rchisq(1500, df = 1, ncp = 550),
                           rchisq(300, df = 1, ncp = 100)), 1800)))

# add some IAT effect by making trials longer in block 6 and 7
rawIATdata[rawIATdata$block >= 6, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6, "latency"] + 100
  
# add some more effect for subjects 1 to 5
rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] <- 
  rawIATdata[rawIATdata$block >= 6 &
             rawIATdata$ID <= 5, "latency"] + 100
  

#### pretreat IAT data using function Pretreatment ####
IATdata <- Pretreatment(rawIATdata,
                             label_subject = "ID",
                          label_latency = "latency",
                          label_accuracy = "correct",
                          label_block = "block",
                          block_pair1 = c(3, 4),
                          block_pair2 = c(6, 7),
                          label_praccrit = "block",
                          block_prac = c(3, 6),
                          block_crit = c(4, 7))


#### Compute reliability for Greenwald et al.'s (2003) D2, D5, and D6 ####
# All scores are computed both with the SplitHalf and with
# the wrappers SplitHalf.D2, SplitHalf.D5, and SplitHalf.D6.


# D2 scores
SplitHalf.D2(IATdata, verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "ignore",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

# D5 scores
SplitHalf.D5(IATdata,
             verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "recode",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

# D6 scores
SplitHalf.D6(IATdata, verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "fxtrim",
             P2 = "recode600",
             P3 = "dscore",
             P4 = "dist",
             verbose = FALSE)

#### Compute reliability for improved scores by Richetin et al. (2015, p. 20) ####
# All scores are computed both with the SplitHalf and with
# the wrappers SplitHalf.D2SWND, SplitHalf.D5SWND, and SplitHalf.D6SWND.
# Results are identical

# D2SWND scores
SplitHalf.D2SWND(IATdata, verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "wins10",
             P2 = "ignore",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

# D5_SWND scores
SplitHalf.D5SWND(IATdata, verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "wins10",
             P2 = "recode",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

# D6_SWND scores
SplitHalf.D6SWND(IATdata, verbose = FALSE)
SplitHalf(IATdata = IATdata,
             P1 = "wins10",
             P2 = "recode600",
             P3 = "dscore",
             P4 = "nodist",
             verbose = FALSE)

IATscores documentation built on July 2, 2020, 3:24 a.m.