Network Analysis for Modeling Complex Systems in SLA Research

Autor/inn/en	Freeborn, Lani; Andringa, Sible; Lunansky, Gabriela; Rispens, Judith
Titel	Network Analysis for Modeling Complex Systems in SLA Research
Quelle	In: Studies in Second Language Acquisition, 45 (2023) 2, S.526-557 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Freeborn, Lani) ORCID (Andringa, Sible)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0272-2631
DOI	10.1017/S0272263122000407
Schlagwörter	Network Analysis; Second Language Learning; Second Language Instruction; Individual Differences; Correlation; Psychological Patterns; Research Methodology; Applied Linguistics; Case Studies; Longitudinal Studies; Systems Approach + Suchen Sie Ihr Suchwort? Netzplantechnik; Zweitsprachenerwerb; Fremdsprachenunterricht; Individueller Unterschied; Korrelation; Research method; Forschungsmethode; Linguistics; Linguistik; Angewandte Linguistik; Case study; Fallstudie; Case Study; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Systemischer Ansatz
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01