You Know What They Say About When You Assume: Assessing the Robustness of Invariant Comparisons

Austin T. Boyd; Karen M. Schmidt; C. S. Bergeman

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You Know What They Say About When You Assume: Assessing the Robustness of Invariant Comparisons

机译：您知道他们在假设时所说的内容：评估不变比较的稳健性

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Early work examining the impact of distributions on item measure estimates was demonstrated to be relatively low (Whitely and Dawis, 1974). More recently, as described in Embretson and Reise (2000), the authors state, "item parameter estimates are not much influenced by the trait distribution in the calibration sample" (p. 146). The extent to which varying trait distribution skewness impacts the item measure parameter estimates is explored in the present analysis. In this paper, we analyzed the Center for Epidemiologic Studies-Depression Scale (CES-D; Radloff, 1977), used in the Notre Dame Study of Health and Well-Being (Bergeman and Deboeck, 2014), with the partial credit model (Masters, 1980) and response shift manipulations. Initial levels of skewness were observed in several items; varying the skewness was shown to greatly impact the item measures, calling into question the robustness of the invariance assumption that estimates are not much influenced by the trait distribution in the calibration sample.

机译：早期工作检查分布对物品措施估计的影响被证明是相对较低的（Whitely和Dawis，1974）。最近，如蜂鸟类和Reise（2000）中所述，作者状态，“项目参数估计不受校准样本中的特质分布的影响”（第146页）。在目前的分析中探讨了不同特质分布偏差影响物品测量参数估计的程度。在本文中，我们分析了流行病学研究中心（CES-D; Radloff，1977），用于健康和福祉（Bergeman和Deboeck，2014）的Notre Dame研究，部分学分模型（硕士，1980年）和响应换档操作。在几件物品中观察到初始倾斜水平;随着偏斜的改变而大大影响物品措施，呼吁质疑不变假设的稳定性，估计的校准样本中的特质分布不大。

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《Journal of applied measurement》 |2020年第2期|190-209|共20页
作者
Austin T. Boyd; Karen M. Schmidt; C. S. Bergeman;
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University of Virginia;

University of Virginia;

University of Notre Dame;

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You Know What They Say About When You Assume: Assessing the Robustness of Invariant Comparisons

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