Modeling (In)Congruence Between Dependent Variables: The Directional and Nondirectional Difference (DNDD) Framework

• 成果类型: Article
• 署名作者: Bednall, Timothy C.; Zhang, Yucheng
• 署名单位: Swinburne University of Technology; Hebei University of Technology
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0000475
• 发表日期: 2020
• 页码: 1013-1035
• 关键词: congruence-as-outcome model; agreement; directional difference; nondirectional difference

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.