Reliability-Targeted Simulation of Item Response Data: Solving the Inverse Design Problem
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909988372348928 |
|---|---|
| author | Lee, JoonHo |
| author_facet | Lee, JoonHo |
| contents | Monte Carlo simulations are the primary methodology for evaluating Item Response Theory (IRT) methods, yet marginal reliability - the fundamental metric of data informativeness - is rarely treated as an explicit design factor. Unlike in multilevel modeling where the intraclass correlation (ICC) is routinely manipulated, IRT studies typically treat reliability as an incidental outcome, creating a "reliability omission" that obscures the signal-to-noise ratio of generated data. To address this gap, we introduce a principled framework for reliability-targeted simulation, transforming reliability from an implicit by-product into a precise input parameter. We formalize the inverse design problem, solving for a global discrimination scaling factor that uniquely achieves a pre-specified target reliability. Two complementary algorithms are proposed: Empirical Quadrature Calibration (EQC) for rapid, deterministic precision, and Stochastic Approximation Calibration (SAC) for rigorous stochastic estimation. A comprehensive validation study across 960 conditions demonstrates that EQC achieves essentially exact calibration, while SAC remains unbiased across non-normal latent distributions and empirical item pools. Furthermore, we clarify the theoretical distinction between average-information and error-variance-based reliability metrics, showing they require different calibration scales due to Jensen's inequality. An accompanying open-source R package, IRTsimrel, enables researchers to standardize reliability as a controlled experimental input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reliability-Targeted Simulation of Item Response Data: Solving the Inverse Design Problem Lee, JoonHo Methodology Computation Monte Carlo simulations are the primary methodology for evaluating Item Response Theory (IRT) methods, yet marginal reliability - the fundamental metric of data informativeness - is rarely treated as an explicit design factor. Unlike in multilevel modeling where the intraclass correlation (ICC) is routinely manipulated, IRT studies typically treat reliability as an incidental outcome, creating a "reliability omission" that obscures the signal-to-noise ratio of generated data. To address this gap, we introduce a principled framework for reliability-targeted simulation, transforming reliability from an implicit by-product into a precise input parameter. We formalize the inverse design problem, solving for a global discrimination scaling factor that uniquely achieves a pre-specified target reliability. Two complementary algorithms are proposed: Empirical Quadrature Calibration (EQC) for rapid, deterministic precision, and Stochastic Approximation Calibration (SAC) for rigorous stochastic estimation. A comprehensive validation study across 960 conditions demonstrates that EQC achieves essentially exact calibration, while SAC remains unbiased across non-normal latent distributions and empirical item pools. Furthermore, we clarify the theoretical distinction between average-information and error-variance-based reliability metrics, showing they require different calibration scales due to Jensen's inequality. An accompanying open-source R package, IRTsimrel, enables researchers to standardize reliability as a controlled experimental input. |
| title | Reliability-Targeted Simulation of Item Response Data: Solving the Inverse Design Problem |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2512.16012 |