使用PARSCALE对评级尺度模型进行参数恢复。

Journal of outcome measurement Pub Date : 1999-01-01
G A French, B G Dodd
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引用次数: 0

摘要

本研究的目的是利用PARSCALE计算机程序对Andrich评定量表模型的项目和特征参数进行恢复。模拟数据矩阵变化的四个因素是(a)项目量表值的分布(倾斜或均匀),(b)类别反应选项的数量(4或5),(c)已知特征水平的分布(正常或倾斜),以及(d)样本量(60、125、250、500或1000)。每个条件被复制10次,得到400个数据矩阵。准确的项目和性状参数估计得到了所有样本量的检验。正如预期的那样,样本量似乎对特质参数的恢复影响不大,但对项目参数的恢复有影响。已知性状水平的分布对项目参数的恢复没有严重影响。得出的结论是,Andrich的评级尺度模型允许使用比通常推荐的其他多尺度IRT模型小得多的校准样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter recovery for the rating scale model using PARSCALE.

The purpose of the present study was to investigate item and trait parameter recovery for Andrich's rating scale model using the PARSCALE computer program. The four factors upon which the simulated data matrices varied were (a) the distribution of the scale values for the items (skewed or uniform), (b) the number of category response options (4 or 5), (c) the distribution of known trait levels (normal or skewed), and (d) the sample size (60, 125, 250, 500, or 1,000). Each condition was replicated 10 times resulting in 400 data matrices. Accurate item and trait parameter estimates were obtained for all sample sizes examined. As expected, sample size seemed to have little influence on the recovery of trait parameters but did influence item parameter recovery. The distribution of known trait levels did not seriously impact the item parameter recovery. It was concluded that Andrich's rating scale model allows for the use of considerably smaller calibration samples than are typically recommended for other polytomous IRT models.

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