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An Investigation of Sample Size Splitting on ATFIND and DIMTEST

机译：在ATFIND和DIMTEST上进行样本量划分的研究

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摘要

Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for empirically deriving a subtest composed of items that potentially measure a second dimension versus DIMTEST for assessing whether this subtest represents a second dimension were investigated. Conditions explored included proportion of sample used for ATFIND, sample size, test length, interability correlations, test structure, and distribution of item difficulties. Overall, it appears that DIMTEST has Type I error rates near the nominal rate and good power in detecting multidimensionality, although Type I error inflation is observed for larger sample sizes. Results suggest that a 50/50 split maximizes power and keeps the Type I error rate below the nominal level unless the test is short and the sample is large. A 75/25 split controls Type I error better for short tests and large samples.
机译：使用一维模型对多维测试数据建模会导致严重的统计错误，例如项目参数估计中的偏差。存在许多用于评估测试的维数的方法。当前的研究集中在DIMTEST。使用模拟数据，研究了将样本大小拆分用于ATFIND程序以凭经验推导子测试的效果，该子测试由可能测量第二维的项目组成，而DIMTEST用于评估该子测试是否代表第二维。探索的条件包括用于ATFIND的样本比例，样本大小，测试长度，交互性相关性，测试结构以及项目难度的分布。总的来说，尽管对于较大的样本量可以观察到I型错误膨胀，但DIMTEST的I型错误率似乎接近标称值，并且在检测多维性方面具有良好的能力。结果表明，除非测试时间短且样品量大，否则以50/50的比例进行分配可最大化功率，并使I型错误率保持在标称水平以下。 75/25拆分控件I型错误更适合短时间测试和大样本。

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