COMPARATIVE STUDY ON THE BOOTSTRAP AND JACKKNIFE METHODS FOR ESTIMATING NON-REGRESSION ESTIMATES
DOI:
https://doi.org/10.60787/tnamp.v22.548Keywords:
Jackknife, Bootstrap, discrimination parameter, difficulty parameter, guessing parameterAbstract
This study compares the performance of Jackknife and Bootstrap resampling methods in estimating parameters of non-linear logistic growth models and the three-parameter logistic Item Response Theory (IRT) model. Both methods produced accurate estimates closely aligned with true parameter values. However, the Bootstrap method consistently demonstrated lower variance, indicating higher precision, especially under conditions of rapid or unstable growth. In the IRT model, Jackknife provided more accurate estimates for Discrimination and Difficulty parameters, while Bootstrap showed better precision overall, though with a slight tendency to underestimate Guessing and Difficulty parameters. Jackknife is preferable when unbiased estimation is critical, particularly in stable data conditions, while Bootstrap is more robust and precise in complex or volatile settings. The study recommends applying Bootstrap in high-stakes or high-variability contexts and emphasizes the importance of understanding both methods to ensure flexible and accurate data analysis in psychometric and modelling research.
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