When starting to formalize psychological constructs, researchers traditionally rely on two distinct approaches: the quantitative approach, which defines constructs as part of a testable theory based on prior research and domain knowledge often deploying self-report questionnaires, or the qualitative approach, which gathers data mostly in the form of text and bases construct definitions on exploratory analyses. Quantitative research might lead to an incomplete understanding of the construct, while qualitative research is limited due to challenges in the systematic data processing, especially at large scale. We present a new computational method that combines the comprehensiveness of qualitative research and the scalability of quantitative analyses to define psychological constructs from semistructured text data. Based on structured questions, participants are prompted to generate sentences reflecting instances of the construct of interest. We apply computational methods to calculate embeddings as numerical representations of the sentences, which we then run through a clustering algorithm to arrive at groupings of sentences as psychologically relevant classes. The method includes steps for the measurement and correction of bias introduced by the data generation, and the assessment of cluster validity according to human judgment. We demonstrate the applicability of our method on an example from emotion regulation. Based on short descriptions of emotion regulation attempts collected through an open-ended situational judgment test, we use our method to derive classes of emotion regulation strategies. Our approach shows how machine learning and psychology can be combined to provide new perspectives on the conceptualization of psychological processes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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一种从文本数据揭示心理结构的计算方法。


当开始正式化心理构造时，研究人员传统上依赖两种不同的方法：定量方法，将构造定义为基于先前研究和领域知识的可测试理论的一部分，通常部署自我报告问卷；或定性方法，收集数据主要以文本形式并基于探索性分析构建定义。定量研究可能会导致对结构的不完整理解，而定性研究则由于系统数据处理（尤其是大规模数据处理）方面的挑战而受到限制。我们提出了一种新的计算方法，结合了定性研究的全面性和定量分析的可扩展性，从半结构化文本数据定义心理结构。根据结构化问题，参与者被提示生成反映兴趣结构实例的句子。我们应用计算方法来计算嵌入作为句子的数字表示，然后通过聚类算法将句子分组为心理相关的类别。该方法包括测量和校正数据生成引入的偏差的步骤，以及根据人类判断评估聚类有效性的步骤。我们通过情绪调节的例子证明了我们的方法的适用性。基于通过开放式情境判断测试收集的情绪调节尝试的简短描述，我们使用我们的方法推导出情绪调节策略的类别。我们的方法展示了如何将机器学习和心理学结合起来，为心理过程的概念化提供新的视角。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。