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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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因变量之间的（in）一致性建模：方向性和非方向性差异 (DNDD) 框架。

本文提出了一种对 2 个因变量之间不一致的前因进行建模的新方法。在这种方法中，不一致被分解为 2 个正交分量，表示方向性和非方向性差异 (DNDD)。无方向差异进一步分为代表共享和独特可变性的组件。我们回顾了以前对差异前因建模的方法，包括使用算术、绝对和平方差，以及 Edwards (1995) 和 Cheung (2009) 的方法。基于 2 项研究，我们证明了 DNDD 方法与其他方法相比的优势。在第一项研究中，我们使用蒙特卡罗模拟来演示每种差异出现的情况，并比较每种方法揭示的见解。在第二项研究中，我们提供了一个使用字段数据集的 DNDD 方法的说明性示例。在讨论中，我们回顾了我们方法的优势和局限性，并提出了几个实际应用。我们的文章提出了基本 DNDD 方法的 2 个扩展，包括使用已知目标或“真实”值对差异进行建模，以及使用多级分析对具有可交换评级的非方向性差异进行建模。（PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。并使用多级分析对具有可交换评级的非方向性差异进行建模。（PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。并使用多级分析对具有可交换评级的非方向性差异进行建模。（PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。