Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions
1990
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Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions
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1990
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Abstract
The sensitivity of marginal maximum likelihood estimation
of item and ability (θ) parameters was examined
when the prior θ distributions are not
matched to the underlying θ distributions. Thirty
sets of 45-item test data were generated by specification
of three types of underlying θ distributions.
They were then analyzed with PC-BILOG. Appropriate
specification of the prior θ distribution increased
the accuracy of estimation for item and θ parameters
when the sample size was large. With a small
dataset, the appropriate specification of the prior
increased the accuracy of θ parameter estimation,
but it did not have that effect on item parameter estimation.
Only with a large dataset and matched underlying
and prior θ distributions did increasing the
number of quadrature points improve the accuracy of
estimation of the item parameters. However, the accuracy
of θ estimation was increased by increasing
the number of quadrature points, regardless of
sample size and appropriateness of the prior θ
distribution. The number of examinees had an important
effect on the accuracy of item parameter
estimation. Index terms: ability distribution, BILOG,
item response theory, marginal maximum likelihood
estimation, parameter estimation, quadrature points.
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Seong, Tae-je. (1990). Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions. Applied Psychological Measurement, 14, 299-311. doi:10.1177/014662169001400307
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doi:10.1177/014662169001400307
Suggested citation
Seong, Tae-je. (1990). Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/113915.
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