Explicit Causal Inference in Personality Research (ECIP)

人格研究中的显式因果推理（ECIP）

• 批准号: 461127198
• 负责人: Professor Dr. Michael Paul Grosz
• 金额: --
• 依托单位: Department Psychologie
• 依托单位国家: 德国
• 项目类别: Scientific Networks
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/461127198?language=en
• 关键词: Explicit; Causal; Inference; Personality; Research

Causal inference is a central goal of science. The preferred method for studying causal effects are experiments. Yet, experiments are often unethical, unfeasible, or very costly in research on personality. Personality researchers have thus rarely applied experimental designs to estimate the causal effects. Causal inference is also possible in nonexperimental studies. However, explicit causal inference on the basis of nonexperimental data has been avoided in personality psychology due to the widespread belief that causal inference is only possible when experimental data are used. Hence, knowledge about causal effects and mechanisms tends to grow at a very slow pace, if at all, in research on personality. This is unfortunate because the shortage of empirical research on causal effects and mechanisms entails a divergence between empirical findings and theories given that theories are usually concerned with causal effects and mechanisms. The shortage of empirical research on causal effects furthermore limits the relevance of personality research for policymakers because knowledge about causal effects is necessary for designing effective and purposive policy interventions, regulations, and laws. The scientific network will promote personality research on causal effects. To do so, we will elaborate on, apply, and demonstrate methods for drawing causal inferences in empirical research on personality. The network will address methodological and conceptual issues surrounding causal inference in personality research by developing methodological/conceptual guidelines and an integrative framework. Furthermore, we will showcase feasible ways to draw explicit causal inferences in personality science by composing empirical studies and grant proposals. By demonstrating how personality researchers can effectively integrate causality into their work, the scientific network will help to reduce the divergence between theory and empirical evidence in research on personality and increase the relevance of personality science for policymakers.

因果推理是科学的核心目标。研究因果效应的首选方法是实验。然而，实验往往是不道德的，不可行的，或非常昂贵的人格研究。因此，人格研究者很少应用实验设计来估计因果效应。在非实验性研究中，因果推理也是可能的。然而，在人格心理学中，基于非实验数据的明确因果推理一直被避免，因为人们普遍认为因果推理只有在使用实验数据时才可能。因此，在人格研究中，关于因果效应和机制的知识往往增长得非常缓慢。这是不幸的，因为缺乏对因果效应和机制的实证研究，导致实证研究结果和理论之间的分歧，因为理论通常涉及因果效应和机制。缺乏对因果效应的实证研究进一步限制了个性研究对政策制定者的相关性，因为有关因果效应的知识对于设计有效和有目的的政策干预，法规和法律是必要的。该科学网络将促进人格因果效应的研究。为了做到这一点，我们将详细阐述，应用，并展示在人格的实证研究中得出因果推论的方法。该网络将通过制定方法/概念指南和综合框架，解决人格研究中因果推理的方法和概念问题。此外，我们将展示可行的方法来得出明确的因果推理，在人格科学组成的实证研究和赠款建议。通过展示人格研究人员如何有效地将因果关系融入他们的工作，科学网络将有助于减少人格研究中理论和经验证据之间的分歧，并增加人格科学对政策制定者的相关性。