Network analysis is a method used to explore the structural relationships between people or organizations, and more recently between psychological constructs. Network analysis is a novel technique that can be used to model psychological constructs that influence language learning as complex systems, with longitudinal data, or cross-sectional data. The majority of complex dynamic systems theory (CDST) research in the field of second language acquisition (SLA) to date has been time-intensive, with a focus on analyzing intraindividual variation with dense longitudinal data collection. The question of how to model systems from a structural perspective using relation-intensive methods is an underexplored dimension of CDST research in applied linguistics. To expand our research agenda, we highlight the potential that psychological networks have for studying individual differences in language learning. We provide two empirical examples of network models using cross-sectional datasets that are publicly available online. We believe that this methodology can complement time-intensive approaches and that it has the potential to contribute to the development of new dimensions of CDST research in applied linguistics.

网络分析是一种用于探索人或组织之间结构关系的方法，最近还用于探索心理结构之间的结构关系。网络分析是一种新技术，可用于将影响语言学习的心理结构建模为具有纵向数据或横截面数据的复杂系统。迄今为止，第二语言习得 (SLA) 领域的大多数复杂动态系统理论 (CDST) 研究都是时间密集型的，重点是通过密集的纵向数据收集来分析个体差异。如何使用关系密集型方法从结构角度对系统建模的问题是应用语言学中 CDST 研究的一个未充分探索的维度。为了扩大我们的研究议程，我们强调心理网络在研究语言学习中的个体差异方面的潜力。我们提供了两个使用可在线公开获得的横截面数据集的网络模型的经验示例。我们相信这种方法可以补充时间密集型方法，并且它有可能为应用语言学中 CDST 研究的新维度的发展做出贡献。