Bias and the effect of priors in Bayesian estimation of parameters of item response models
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Bias and the effect of priors in Bayesian estimation of parameters of item response models
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1990
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Abstract
The effectiveness of a Bayesian approach to the estimation
problem in item response models has been
sufficiently documented in recent years. Although research
has indicated that Bayesian estimates, in general,
are more accurate than joint maximum likelihood
(JML) estimates, the effect of choice of priors on the
Bayesian estimates is not well known. Moreover, the
extent to which the Bayesian estimates are biased in
comparison with JML estimates is not known. The effect of priors and the amount of bias in Bayesian estimates
is examined in this paper through simulation
studies. It is shown that different specifications of
prior information have relatively modest effects on the
Bayesian estimates. For small samples, it is shown
that the Bayesian estimates are less biased than their
JML counterparts. Index terms: accuracy, Bayesian
estimates, bias, item response models, joint maximum
likelihood estimates, priors.
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Gifford, Janice A & Swaminathan, Hariharan. (1990). Bias and the effect of priors in Bayesian estimation of parameters of item response models. Applied Psychological Measurement, 14, 33-43. doi:10.1177/014662169001400104
Suggested citation
Gifford, Janice A.; Swaminathan, Hariharan. (1990). Bias and the effect of priors in Bayesian estimation of parameters of item response models. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/107736.
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