Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions

Seong, Tae-je
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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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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Applied Psychological Measurement, Volume 14, 1990

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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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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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