Our findings reveal significant differences in model performance, with OpenAI’s GPT-text-embedding
Our findings reveal significant differences in model performance, with OpenAI’s GPT-text-embedding
Our findings reveal significant differences in model performance, with OpenAI’s GPT-text-embedding
Our findings reveal significant differences in model performance, with OpenAI’s GPT-text-embedding
and argumentative writing skills, as assumed in the student–feedback interaction model
and argumentative writing skills, as assumed in the student–feedback interaction model
and argumentative writing skills, as assumed in the student–feedback interaction model
and argumentative writing skills, as assumed in the student–feedback interaction model
More stable estimation of the STARTS model: A Bayesian approach using Markov Chain
Implications for teacher education and classroom practice such as the emulation of model
Implications for teacher education and classroom practice such as the emulation of model
Implications for teacher education and classroom practice such as the emulation of model
Implications for teacher education and classroom practice such as the emulation of model
This study evaluates the simulation extrapolation (SIMEX) method as a bias-correction approach for the distribution-weighted and difficulty-weighted root mean square deviation (RMSD) item fit statistics. The results indicate that SIMEX reduces the positive bias of the original RMSD statistic and can be applied in the context of differential item functioning (DIF) analysis. Although the SIMEX-based RMSD statistics showed slightly greater bias than previously proposed analytic corrections, they yielded lower RMSE for items with DIF. For items without DIF, the analytic bias-correction methods performed better with respect to both bias and root mean square error (RMSE). An empirical example further showed that the SIMEX-based and analytically bias-corrected RMSD statistics produced very similar estimates.
Veröffentlicht – 06.2026 Keywords item fit, differential item functioning, item response model
This study evaluates the simulation extrapolation (SIMEX) method as a bias-correction approach for the distribution-weighted and difficulty-weighted root mean square deviation (RMSD) item fit statistics. The results indicate that SIMEX reduces the positive bias of the original RMSD statistic and can be applied in the context of differential item functioning (DIF) analysis. Although the SIMEX-based RMSD statistics showed slightly greater bias than previously proposed analytic corrections, they yielded lower RMSE for items with DIF. For items without DIF, the analytic bias-correction methods performed better with respect to both bias and root mean square error (RMSE). An empirical example further showed that the SIMEX-based and analytically bias-corrected RMSD statistics produced very similar estimates.
Veröffentlicht – 06.2026 Keywords item fit, differential item functioning, item response model
Robust mean–geometric mean (MGM) linking is a method for comparing the performance of two groups on a test involving dichotomous items and is particularly suited to settings with fixed and sparse differential item functioning (DIF). However, robust MGM linking has been shown to yield biased estimates in finite samples because the estimated item parameters are affected by sampling error, which in turn induces bias in the estimated linking parameters. To address this issue, the simulation extrapolation (SIMEX) method is applied to robust MGM linking to reduce bias in the linking parameter estimates. Results from a simulation study demonstrate that SIMEX reduces bias in robust MGM linking. Moreover, SIMEX with a linear extrapolation function also reduces the variance of the parameter estimates in the absence of DIF effects. These findings indicate that the application of SIMEX in robust MGM linking methods can be generally recommended for empirical research aimed at removing DIF items from group comparisons.
Keywords differential item functioning, mean–geometric mean linking, item response model
