SOFTWARE & APPLETS
PowerUpR: Power Analysis Tools for Multilevel Randomized Experiments
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes. Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>), five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect, and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
Please cite as:
Bulus, M., Dong, N., Kelcey, B., & Spybrook, J. (2021). PowerUpR: Power Analysis Tools for Multilevel Randomized Experiments. R package version 1.1.0. https://cran.r-project.org/package=PowerUpR
cosa: Bound Constrained Optimal Sample Size Allocation
Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.
Please cite as:
Bulus, M. (2021). Minimum detectable effect size computations for cluster-level regression discontinuity: Specifications beyond the linear functional form. Journal of Research on Education Effectiveness, 15 (1), 151-177. https://doi.org/10.1080/19345747.2021.1947425
Bulus, M., & Dong, N. (2021). Bound constrained optimization of sample sizes subject to monetary restrictions in planning multilevel randomized trials and regression discontinuity studies. The Journal of Experimental Education, 89(2):379-401. https://doi.org/10.1080/00220973.2019.1636197
Bulus, M. & Dong, N. (2021). cosa: Bound Constrained Optimal Sample Size Allocation. R package version 2.1.0. https://cran.r-project.org/package=cosa
irtDemo: Item Response Theory Demo Collection
Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models.
Please cite as:
Bulus, M., & Bonifay, W. (2022). irtDemo R Package: Pedagogical Interactive Web Applications for Estimation, Scoring, and Multi Dimensionality in Item Response Theory. Anadolu University Journal of Education Faculty, 6(1), 92-108. https://doi.org/10.34056/aujef.913781
Bulus, M. & Bonifay, W. (2018). irtDemo: Item Response Theory Demo Collection. R package version 0.1.4. https://github.com/metinbulus/irtDemo