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In:Research Methods in Complex Dynamic Systems Theory Approaches to Second Language Development
Edited by Wander Lowie, Rosmawati and Vanessa De Wilde
[Research Methods in Applied Linguistics 14] 2025
► pp. 143–166

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Chapter 8
Unravelling the dynamics of language learning

Insights from temporal network analysis

 | University of Amsterdam
 | University of Marburg
Published online: 11 September 2025
DOI logo with link
https://doi.org/10.1075/rmal.14.08fre
Abstract
In this chapter, we use temporal network analysis to model longitudinal associations among six variables. We present three different networks to illustrate the threefold framework that temporal network analysis offers for analysing time-series data (Blanchard et al., 2023): a temporal network (how variables predict one another from one timepoint to the next), a contemporaneous network (how variables are related within the same timeframe), and a between-subjects network (how variables are related across individuals). Conceptually, a network approach has many affordances for CDST-inspired research. Temporal network analysis is an exploratory, data-driven method that is conceptually aligned with many aspects of CDST. Methodologically however, temporal network analysis presents several challenges, including the large number of data points needed, assumptions of linearity, and considerations for handling missing data.
Article outline
  • Introduction
  • Network analysis
  • Temporal network analysis
  • The empirical study
    • Method
      • Participants
      • Procedure
      • Pre-processing of data
      • Reflection on research ethics
      • Materials
      • Analyses
      • Distributions of variables
      • Stationarity
    • Results
      • Descriptive statistics
      • Network analysis
      • Temporal network
      • Contemporaneous network
      • Between-subjects network
      • Stability analyses
  • Affordances of temporal network analysis
  • Limitations of temporal network analysis
  • Conclusion
  • Notes
  • References
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