Skip to contents

Simulated 1-3-3 MST Panel Data

Source: R/simMST.R
simMST.Rd

A simulated Multistage-Adaptive Test (MST) dataset representing a 1-3-3 panel structure, assembled using module-level target test information functions (TIFs) and design constraints similar to those used in the simulation study by Lim et al. (2021).

Usage

simMST

Format

A list containing five internal objects:

item_bank

A data frame of item metadata for the 56 items assigned to the panel (one row per item; columns follow the irtQ item metadata format: id, cats, model, par.1, par.2, par.3).

module

A binary integer matrix (56 x 7) that maps items in the item bank to MST modules. This structure specifies the item-to-module assignment, similar to the modules argument in the randomMST() function from the mstR package (Magis et al., 2017).

route_map

A binary square integer matrix (7 x 7) that defines the MST transition structure, showing module pathways across stages. Module 1 routes to modules 2 to 4, module 2 to modules 5 and 6, module 3 to modules 5 to 7, and module 4 to modules 6 and 7. This corresponds to the transMatrix argument in the randomMST() function in the mstR package.

cut_score

A list of two numeric vectors, stage.2 and stage.3, giving the routing cut scores into stages 2 and 3. They are the TIF-crossing points in the cut_score element returned by find_cut(x = simMST$item_bank, module = simMST$module, route_map = simMST$route_map, D = 1.702).

theta

A numeric vector of 81 ability values from -4 to 4 in steps of 0.1, used to evaluate the measurement precision of the panel across the latent trait continuum.

This 1-3-3 MST panel includes 7 modules across 3 stages, drawn from 56 unique items (8 per module) with no item shared across modules. Each module contains 8 dichotomously scored items calibrated under the IRT 3-parameter logistic (3PL) model. The item parameters are on the D = 1.702 scale, so pass D = 1.702 to find_cut(), reval_mst(), and run_mst() when using this panel.

References

Lim, H., Davey, T., & Wells, C. S. (2021). A recursion-based analytical approach to evaluate the performance of MST. Journal of Educational Measurement, 58(2), 154-178. doi:10.1111/jedm.12276 .

Magis, D., Yan, D., & von Davier, A. A. (2017). Computerized adaptive and multistage testing with R: Using packages catR and mstR. Springer. doi:10.1007/978-3-319-69218-0 .

Author

Hwanggyu Lim hglim83@gmail.com

Examples

# structure of the data
str(simMST, max.level = 1)
#> List of 5
#>  $ item_bank:'data.frame':	56 obs. of  6 variables:
#>  $ module   : int [1:56, 1:7] 0 0 0 1 0 0 0 0 0 0 ...
#>  $ route_map: int [1:7, 1:7] 0 0 0 0 0 0 0 1 0 0 ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $ cut_score:List of 2
#>  $ theta    : num [1:81] -4 -3.9 -3.8 -3.7 -3.6 -3.5 -3.4 -3.3 -3.2 -3.1 ...

# item metadata of the first three items
head(simMST$item_bank, 3)
#>   id cats model     par.1     par.2      par.3
#> 1  1    2  3PLM 0.8046689 0.8887742 0.07111049
#> 2  2    2  3PLM 1.1698814 1.9293569 0.09127957
#> 3  3    2  3PLM 1.0684706 0.4136401 0.11193439