Bootstrap-Calibrated Interval Estimates for Latent Variable Scores in Item Response Theory

@article{Liu2018BootstrapCalibratedIE,
  title={Bootstrap-Calibrated Interval Estimates for Latent Variable Scores in Item Response Theory},
  author={Yang Liu and Ji Seung Yang},
  journal={Psychometrika},
  year={2018},
  volume={83},
  pages={333-354}
}
In most item response theory applications, model parameters need to be first calibrated from sample data. Latent variable (LV) scores calculated using estimated parameters are thus subject to sampling error inherited from the calibration stage. In this article, we propose a resampling-based method, namely bootstrap calibration (BC), to reduce the impact of the carryover sampling error on the interval estimates of LV scores. BC modifies the quantile of the plug-in posterior, i.e., the posterior… CONTINUE READING
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