Computational Aspects of L 0 Linking in the Rasch Model.

Algorithms. Apr2025, Vol. 18 Issue 4, p213. 18p.

The L 0 linking approach replaces the L 2 loss function in mean–mean linking under the Rasch model with the L 0 loss function. Using the L 0 loss function offers the advantage of potential robustness against fixed differential item functioning effects. However, its nondifferentiability necessitates differentiable approximations to ensure feasible and computationally stable estimation. This article examines alternative specifications of two approximations, each controlled by a tuning parameter ε that determines the approximation error. Results demonstrate that the optimal ε value minimizing the RMSE of the linking parameter estimate depends on the magnitude of DIF effects, the number of items, and the sample size. A data-driven selection of ε outperformed a fixed ε across all conditions in both a numerical illustration and a simulation study. [ABSTRACT FROM AUTHOR]

*Rasch models, *Approximation error, *Sample size (Statistics), *Computer simulation

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