Complex Dynamic Systems Research: Some Insights on Data Analysis

Abstract

Acquiring a second language (L2) has been increasingly recognized to be an ongoing developmental process, one that progresses in a fluid and non-linear fashion (Larsen-Freeman, 2015). Instead of operating like a mechanical black box that parses incoming linguistic information and outputs oral/written language indiscriminately, the learner’s developing system has been shown to be selectively adaptive (e.g., Han & Selinker, 1999; Schachter, 1974; Slobin, 1996). Where variability in acquisitional data arises, more than one of the following factors are likely involved: crosslinguistic influence, avoidance, conceptual transfer, among others. For this reason, research adopting a Complex Dynamic Systems approach to data analysis has gained sway in Second Language Acquisition (SLA) in recent years. Unlike most existing empirical L2 acquisition studies that employ only a single method of data analysis, L2 Complex Dynamic Systems research advocates the application of multiple data analysis methods to the same set(s) of empirical data. In doing so, researchers are more likely to gain a multidimensional and finer-grained grasp of what the empirical L2 data might in fact be suggesting about the learner’s developmental trajectory.