Check Datasets for Deviations From Test Design

This function checks whether a data frame corresponds to a particular rotated block design, i.e. whether all persons have valid codes on all items they were presented with and one consistent missing code for all items they were not presented with.

Usage

checkDesign(dat, booklets, blocks, rotation, sysMis="NA", id="ID",
    subunits = NULL, verbose = TRUE)

Arguments

dat: A data frame
booklets: A data frame containing information about the number of blocks in each booklet and the names of these blocks with column names 'subunit', 'block', 'subunitBlockPosition'. See ‘Examples’.
blocks: A data frame containing the names of subunits and their order within each block with column names 'booklet', 'block1', 'block2' ... etc. See ‘Examples’.
rotation: A data frame containing information about which participant worked on which booklet with column names [id as specified above], 'booklet'. See ‘Examples’.
sysMis: A character vector of length 1 indicating the missing code for items that were not administered to a participant. Default is "NA".
id: A character vector of length 1 indicating the name of the participant identifier variable in dat. Default is ID.
subunits: Optional: A data frame with subunit information (c.f. inputList). This data frame is used to identify the names of recoded subunits.
verbose: logical. If FALSE, no information is printed.

Author

Karoline Sachse, Philipp Franikowski

Examples

data(inputDat)
data(inputList)

prepDat <- automateDataPreparation (inputList = inputList, datList = inputDat,
    readSpss = FALSE, checkData = FALSE, mergeData = TRUE, recodeData = TRUE,
    aggregateData = FALSE, scoreData = FALSE, writeSpss = FALSE, verbose = TRUE)
#> Starting automateDataPreparation 2024-11-15 13:42:20.068522
#> 
#> Check has been skipped.
#> 
#> Start merging.
#> Start merging of dataset 1.
#> Start merging of dataset 2.
#> Start merging of dataset 3.
#> Start adding mbd according to data pattern.
#> 
#> Start recoding.
#> 
#> Found no recode information for variable(s): 
#> ID, hisei. 
#> This/These variable(s) will not be recoded.
#> 
#> Variables... I01, I02, I03, I04, I05, I06, I07, I08, I09, I10, I11, I12a, I12b, I12c, I13, I14, I15, I16, I17, I18, I19, I20, I21, I22, I23, I24, I25, I26, I27, I28
#> ...have been recoded.
#> 
#> RecodeMnr has been skipped.
#> 
#> Aggregate has been skipped.
#> 
#> Scoring has been skipped.
#> 
#> No SPSS-File has been written.
#> 
#> Missings are UNcollapsed.
#> automateDataPreparation terminated successfully! 2024-11-15 13:42:20.16629

checkDesign(dat = prepDat, booklets = inputList$booklets, blocks = inputList$blocks,
    rotation = inputList$rotation, sysMis = "mbd", id="ID",
    subunits = inputList$subunits, verbose = TRUE)
#> 
#> ── Check: Subunit recoding 
#> ℹ Use names for recoded subunits.
#> 
#> ── Check: Variables in the dataset 
#> ℹ The following 1 variable is not in info (`subunit` in blocks) but in dataset.
#> It will be ignored during check: `hisei`
#> 
#> ── Check: Valid and missing codes 
#> ✔ No deviations from design detected!

prepDat[1:7,"I22R"] <- "1"
prepDat[1:4,c("I01R","I02R","I03R")] <- "mbd"

checkDesign(dat = prepDat, booklets = inputList$booklets, blocks = inputList$blocks,
    rotation = inputList$rotation, sysMis = "mbd", id="ID",
    subunits = inputList$subunits, verbose = TRUE)
#> 
#> ── Check: Subunit recoding 
#> ℹ Use names for recoded subunits.
#> 
#> ── Check: Variables in the dataset 
#> ℹ The following 1 variable is not in info (`subunit` in blocks) but in dataset.
#> It will be ignored during check: `hisei`
#> 
#> ── Check: Valid and missing codes 
#> ℹ Deviations from design detected!
#> ✖ Found for 3 subunits sysMis instead of valid codes for booklet `booklet1`:
#> I01R, I02R, and I03R
#> ℹ I01R, I02R, I03R (4 cases): person100, person101, person102, person103
#> ✖ Found for 1 subunit valid codes instead of sysMis for booklet `booklet1`:
#> I22R
#> ℹ I22R (7 cases): person100, person101, person102, person103, person104, person105, person106