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Statistical versus substantive dimensionality - The effect of distributional differences on dimensionality assessment using DIMTEST

机译:统计维数与实质维数-分布差异对使用DIMTEST进行维数评估的影响

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摘要

It is believed by some that most tests are multidimensional, meaning that they measure more than one underlying construct. The primary objective of this study is to illustrate how variations in the secondary ability distribution affect the statistical detection of dimensionality and to demonstrate the difference between substantive and statistical dimensionality. Given dichotomous data simulated to be multidimensional, this study shows how varying the ability distributions affects the results obtained from DIMTEST, a nonparametric statistical procedure based on the theory of essential unidimensionality. Results indicate that the power of DIMTEST decreased as the mean of the secondary ability distribution approached the extremes and/or as the standard deviation of the secondary ability distribution approached zero. This has important implications for both researchers and practitioners because although a test may measure additional dimensions from a substantive viewpoint, these dimensions may not be detected statistically.
机译:一些人认为大多数测试都是多维的,这意味着它们可以测量多个基础构造。这项研究的主要目的是说明二级能力分布的变化如何影响维度的统计检测,并证明实体维度和统计维度之间的差异。给定模拟为多维的二分数据,本研究表明能力分布的变化如何影响从DIMTEST获得的结果,DIMTEST是基于基本一维性理论的非参数统计程序。结果表明,随着二级能力分布的平均值接近极限和/或二级能力分布的标准偏差接近零,DIMTEST的功效降低。这对研究人员和从业人员都具有重要意义,因为尽管测试可能会从实质性角度衡量其他维度,但这些维度可能无法进行统计检测。

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