Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.
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DOI:
10.1027/1015-5759/a000612
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发表时间:
2020
期刊:
European journal of psychological assessment : official organ of the European Association of Psychological Assessment
影响因子:
--
通讯作者:
Molenaar PCM
Molenaar PCM
中科院分区:
其他
文献类型:
--
作者:
Park JJ;Chow SM;Fisher ZF;Molenaar PCM

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近年来,动态网络模型的使用有所增长。这些模型使研究人员能够捕获纵向数据中的滞后效应和同期效应,这些效应通常是标准向量自回归 (VAR) 模型的变化、重新表述或扩展。迄今为止,许多动态网络尚未被明确地相互比较。我们比较了三种流行的动态网络方法——GIMME、uSEM 和 LASSO gVAR——它们在建模假设、估计程序、基于蒙特卡罗模拟的统计特性以及对情感和人格研究人员的影响方面的差异。我们发现,动态网络的所有三种方法都提供了群体层面的实证结果,部分支持情感和人格理论。然而,个人层面的结果揭示了方法和参与者之间存在很大的异质性。我们讨论了差异的原因以及这些方法各自的优点和局限性。
The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches–GIMME, uSEM, and LASSO gVAR–in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.
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