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首页> 外文期刊>Journal of applied measurement >The Impact of Data Collection Design, Linking Method, and Sample Size on Vertical Scaling Using the Rasch Model
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The Impact of Data Collection Design, Linking Method, and Sample Size on Vertical Scaling Using the Rasch Model

机译：数据收集设计，链接方法和样本大小对使用Rasch模型的垂直缩放的影响

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The Rasch model-based vertical scaling was evaluated by simulation study with respect to recovery of item parameter, linking constant, population mean (grade-to-grade growth), population standard deviation (grade-to-grade variability), and separation of grade distributions by effect size. The simulated vertical scale had five different grades with five different test levels. Controlled factors were data collection design, linking methods, and sample size. For item parameter, linking constant, and population mean, counter-balanced single group (CBSG) with mean/mean (or fixed item) method and concurrent calibration performed best. The population standard deviation recovery, as sample size increases, did not show systematic improvement across different data collection and linking methods. For the separation of grade distributions, CBSG with mean/mean (or fixed item) methods performed best. The average absolute differences from the true parameters were less than 0.1 in logit across different linking methods. In general the differences between different linking methods were less than those between different sample sizes.
机译：通过模拟研究评估了基于Rasch模型的垂直缩放比例，这些参数涉及项参数的恢复，链接常数，总体平均值（等级间的增长），总体标准差（等级间的变异性）和等级分离效果大小的分布。模拟的垂直标尺具有五个不同的等级以及五个不同的测试等级。控制因素是数据收集设计，链接方法和样本量。对于项目参数，将常数和总体均值联系起来，采用均值/均值（或固定项目）方法的平衡单组（CBSG）和同时进行的校准效果最佳。随着样本量的增加，总体标准偏差的恢复未显示出跨不同数据收集和链接方法的系统性改善。为了分离等级分布，采用均值/均值（或固定项目）方法的CBSG效果最好。在不同的链接方法中，与真实参数的平均绝对差在logit上小于0.1。通常，不同链接方法之间的差异小于不同样本大小之间的差异。

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