The testRetestCES function computes the test-retest Coefficient of Equivalence and Stability (Schmidt, Le & Ilies, 2003).

```
testRetestCES(
dat = NULL,
moments = NULL,
testDat = NULL,
retestDat = NULL,
parallelTests = "means",
sortItems = FALSE,
convertToNumeric = TRUE,
digits = 4
)
# S3 method for testRetestCES
print(x, digits = x$input$digits, ...)
```

- dat
A dataframe. For testRetestCES, this dataframe must contain the items in the scale at both measurement moments. If no dataframe is specified, a dialogue will be launched to allow the user to select an SPSS datafile. If only one dataframe is specified, either the items have to be ordered chronologically (i.e. first all items for the first measurement, then all items for the second measurement), or the vector 'moments' has to be used to indicate, for each item, to which measurement moment it belongs. The number of columns in this dataframe MUST be even! Note that instead of providing this dataframe, the items of each measurement moment can be provided separately in testDat and retestDat as well.

- moments
Used to indicate to which measurement moment each item in 'dat' belongs; should be a vector with the same length as dat has columns, and with two possible values (e.g. 1 and 2).

- testDat, retestDat
Dataframes with the items for each measurement moment: note that the items have to be in the same order (unless sortItems is TRUE).

- parallelTests
A vector indicating which items belong to which parallel test; like the moments vector, this should have two possible values (e.g. 1 and 2). Alternatively, it can be character value with 'means' or 'variances'; in this case, parallelSubscales will be used to create roughly parallel halves.

- sortItems
If true, the columns (items) in each dataframe are ordered alphabetically before starting. This can be convenient to ensure that the order of the items at each measurement moment is the same.

- convertToNumeric
When TRUE, the function will attempt to convert all vectors in the dataframes to numeric.

- digits
Number of digits to print.

- x
The object to print

- ...
Ignored.

An object with the input and several output variables. Most notably:

- input
Input specified when calling the function

- intermediate
Intermediate values and objects computed to get to the final results

- output$testRetestCES
The value of the test-retest Coefficient of Equivalence and Stability.

This function computes the test-retest Coefficient of Equivalence and Stability (CES) as described in Schmidt, Le & Ilies (2003). Note that this function only computes the test-retest CES for a scale that is administered twice and split into two parallel halves post-hoc (this procedure is explained on page 210, and the equations that are used, 16 and 17a are explained on page 212).

This function uses equations 16 and 17 on page 212 of Schmidt, Le & Ilies (2003): in other words, this function assumes that one scale is administered twice. If you'd like the computation for two different but parellel scales/measures to be implemented, please contact me.

Schmidt, F. L., Le, H., & Ilies, R. (2003) Beyond Alpha: An Empirical Examination of the Effects of Different Sources of Measurement Error on Reliability Estimates for Measures of Individual-differences Constructs. Psychological Methods, 8(2), 206-224. doi:10/dzmk7n

```
if (FALSE) {
### This will prompt the user to select an SPSS file
testRetestCES();
}
### Load data from simulated dataset testRetestSimData (which
### satisfies essential tau-equivalence).
data(testRetestSimData);
### The first column is the true score, so it's excluded in this example.
exampleData <- testRetestSimData[, 2:ncol(testRetestSimData)];
### Compute test-retest alpha coefficient
testRetestCES(exampleData);
#> Coefficient of Equivalence and Stability: 0.428
#>
#> To help assess whether the subscales (automatically generated using means) are parallel, here are the means and variances:
#> Mean subscale a1, time 1: 109.2926 (variance = 219.5932)
#> Mean subscale a2, time 1: 167.7709 (variance = 556.0309)
#> Mean subscale a1, time 2: 110.8976 (variance = 189.5549)
#> Mean subscale a2, time 2: 170.8491 (variance = 613.3707)
```