# Splitting Methods

    library(splithalfr)

This vignette demonstrates the methods of splitting data that are supported by the `splithalfr`. Each splitting method is illustrated by calling `by_split` with the right arguments, printing to the terminal what data is in each of the two parts produced by a split. For a comprehensive review of each splitting method, see Pronk et al. (2021).

# Example data

We’ll use this example dataset with eight trials of one participant, each trial having a condition and rt variable.

    ds <- data.frame(
      participant = rep(1, 8),
      condition = rep(c("a", "b"), each = 4),
      rt = 100 * 1 : 8
    )

# First-second splitting

First-second splitting assigns trials of the first half of rows to one part and trials of the second half of rows to the other (Green et al., 2016; Webb, Shavelson, & Haertel, 1996; Williams & Kaufmann, 2012). For this splitting method, set `method` to `first_second`.

    dummy = by_split(
      ds,
      ds$participant,
      method = "first_second",
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )

# Odd-even splitting

Odd-even splitting assigns trials with an odd row number to one part and trials with an even row number to the other (Green et al., 2016; Webb, Shavelson, & Haertel, 1996; Williams & Kaufmann, 2012). For this splitting method, set `method` to `odd_even`.

    dummy = by_split(
      ds,
      ds$participant,
      method = "odd_even",
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )

# Permutated splitting

Permutated splitting is also known as random splitting (Kopp, Lange, & Steinke, 2021), bootstrapped splitting (Parsons, Kruijt, & Fox, 2019) and random sample of split halves (Williams & Kaufmann, 2012). It assigns trials to each part via random sampling without replacement. This splitting method is the default, but you can make it explicit by setting `method` to `random`. In practice, random splits are averaged over many replications, but for illustration we’re only printing one.

    dummy = by_split(
      ds,
      ds$participant,
      method = "random",
      replications = 1,
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )

# Monte Carlo splitting

Monte Carlo splitting assigns trials to each part by sampling with replacement (Williams & Kaufmann, 2012). For constructing parts that are of any length, use the `split_p` argument and set `replace` to `TRUE`. The example below constructs two parts of the same length as the original dataset by setting `split_p` to 1.

    dummy = by_split(
      ds,
      ds$participant,
      method = "random",
      replace = TRUE,
      split_p = 1,
      replications = 1,
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )

# Stratified splitting

If a split is stratified by a variable, then trials are separately assigned to each part for each level of that variable (Green et al., 2016). For example, if splits are stratified by `ds$condition`, the trials with condition a and b are split separately. Stratification can be used in combination with any of the methods above. For illustration we combine it with first-second splitting

    dummy = by_split(
      ds,
      ds$participant,
      method = "first_second",
      stratification = ds$condition,
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )

# Subsampled splitting

In a subsampled split, a subset of the trials is randomly sampled without replacement and then split (see the supplementary materials of Hedge, Powell, & Sumner, 2018). Sub-sampling only works well with splitting methods that uses random sampling (permutated and Monte Carlo). Since the sub-sampling procedure already randomizes the trials selected for splitting, splitting methods that assign trials to part based on their row number, such as first-second and odd-even, should give results that are similar to permutated splitting. Any stratifications are applied both to the sub-sampling and splitting.

    dummy = by_split(
      ds,
      ds$participant,
      method = "random",
      stratification = ds$condition,
      subsample_p = 0.5,
      function(ds) { print(ds); },
      ncores = 1,
      verbose = F
    )