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首页> 外文期刊>Journal of Applied Psychology >Modeling (In)Congruence Between Dependent Variables: The Directional and Nondirectional Difference (DNDD) Framework
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Modeling (In)Congruence Between Dependent Variables: The Directional and Nondirectional Difference (DNDD) Framework

机译：依赖变量之间的建模（IN）同时：定向和非触点差异（DNDD）框架

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摘要

This article proposes a new approach to modeling the antecedents of incongruence between 2 dependent variables. In this approach, incongruence is decomposed into 2 orthogonal components representing directional and nondirectional difference (DNDD). Nondirectional difference is further divided into components representing shared and unique variability. We review previous approaches to modeling antecedents of difference, including the use of arithmetic, absolute, and squared differences, as well as the approaches of Edwards (1995) and Cheung (2009). Based on 2 studies, we demonstrate the advantages of DNDD approach compared with other methods. In the first study, we use a Monte Carlo simulation to demonstrate the circumstances under which each type of difference arises, and we compare the insights revealed by each approach. In the second study, we provide an illustrative example of DNDD approach using a field dataset. In the discussion, we review the strengths and limitations of our approach and propose several practical applications. Our article proposes 2 extensions to the basic DNDD approach, including modeling difference with a known target or "true" value, and using multilevel analysis to model nondirectional difference with exchangeable ratings.
机译：本文提出了一种新的方法来建模了2个依赖变量之间的不协调的前提。在这种方法中，Incomence被分解为表示定向和非向差异（DNDD）的2个正交分量。非向差异进一步分为代表共享和独特变异性的组件。我们审查以前的差异建模方法，包括使用算术，绝对和平方差异，以及爱德华州（1995）和Cheung（2009）的方法。基于2项研究，我们展示了与其他方法相比DNDD方法的优势。在第一项研究中，我们使用蒙特卡罗模拟来证明所产生的每种类型的差异的情况，并且我们比较每个方法所显示的见解。在第二研究中，我们提供了使用现场数据集的DNDD方法的说明性示例。在讨论中，我们审查了我们方法的优势和局限性，并提出了几种实际应用。我们的文章提出了基本DNDD方法的2个扩展，包括与已知目标或“真实”值的建模差异，并使用多级分析与可交换额定值模拟非向差异。

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