Network analysis for modeling complex systems in SLA research

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,...

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Bibliographic Details
Published in:Studies in second language acquisition 2023-05, Vol.45 (2), p.526-557
Main Authors: Freeborn, Lani, Andringa, Sible, Lunansky, Gabriela, Rispens, Judith
Format: Article
Language:English
Subjects:
Applied Linguistics
Case Studies
Classrooms
Cognitive Development
Complexity
Correlation
Data collection
Individual Differences
Language
Language Acquisition
Linguistics
Longitudinal Studies
Methods Forum
Network Analysis
Psychological Patterns
Research Methodology
Researchers
Second Language Instruction
Second Language Learning
Social network analysis
Social networks
Study abroad
System theory
Systems Approach
Variables
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Summary: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.
ISSN:0272-2631
1470-1545
DOI:10.1017/S0272263122000407