The emergence of complex dynamic systems theory (CDST) has brought challenges and innovation in theory, research, and practice of language teaching and learning. To date, however, methodological developments have lagged behind (Hiver & Al Hoorie, 2020). Change point analysis (CPA) is a statistical method appropriate for analyzing dynamic data in language development. The method has been used in studies of groups (Han & Hiver, 2018) and individuals (Nitta & Baba, 2015). CPA identifies points or ‘thresholds’ along a distribution of values on either side of which characteristics vary significantly. CPA is used in time series data where parametric assumptions cannot be made (Taylor, 2000). Because it focuses on change, CPA is particularly useful in studies from a CDST perspective where research questions address changes either in the means or variance of variables without making untenable parametric assumptions. In this presentation we will demonstrate the empirical use of CPA software in two studies of willingness to communicate (WTC) with individual-level data. WTC is defined as the readiness to talk to a specific person in a specific context using the L2 (or L3, etc.) emerging from interactions within a dynamic system (MacIntyre, 2020). It has been argued that sources of WTC can best be understood by intensive analysis of individual cases, analysis that is “conspicuously absent” from the literature (Friermuth & Ito, 2020). We focus on how CPA was used to identify specific thresholds indicating reliable changes in WTC. Study 1 adopted a fieldwork approach to examine long-term changes in WTC among six immigrants to Sweden over their first year in-country. In Study 2, CPA identified changes in WTC among 10 ESL students in Canada who were discussing a meaningful photo in a language lab context. In both studies, CPA focused analysis on identifying the specific processes leading to changes in WTC.