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. 80–101
Chapter 5A latent transition analysis of Chinese EFL learners’ motivational
profiles
Configurations, stability, and transitions
Published online: 11 September 2025
https://doi.org/10.1075/rmal.14.05pen
https://doi.org/10.1075/rmal.14.05pen
Abstract
This chapter addresses the limitations of traditional
variable-centered approaches in capturing the complexity of second language
(L2) motivation, by adopting a person-centered, longitudinal framework.
Specifically, it introduces latent transition analysis (LTA) as a powerful
analytic technique for modeling motivational change over time, scrutinizing
learners’ motivational configurations and dynamics. In this chapter, we
first outline the paradigm shift toward person-centered research in L2
motivation and explain key methodological and statistical elements of LTA.
We then demonstrate how to identify latent motivational profiles among
Chinese EFL learners and model their transitions over time using Mplus. The
chapter concludes with methodological reflections on applying this Complex
Dynamic Systems Theory (CDST)-informed approach, highlighting challenges and
best practices for analyzing L2 learning and development.
Article outline
- Introduction
- Paradigm shift toward a dynamic, holistic perspective on L2 motivation
- Adopting person-centered approaches to L2 motivation
- LTA: A step-by-step procedure
- Step 0: Examining descriptive statistics
- Step 1: Uncovering latent profiles for each time point
- Step 2: Ensuring fair comparisons across time points
- Step 3: Mapping transitions between profiles across time points
- Step 4: Including covariates in the LTA Model (optional)
- Empirical illustration: LTA for L2 motivational structure and dynamics
- Participants
- Measures
- Data analysis
- Identifying typical motivational profiles for each time point
- Testing profile similarity based on time-specific LPAs results
- Modeling transitions between profiles across time points
- Results
- Time-specific LPAs: Four profiles identified
- Characteristics of the four motivation profiles
- Stability and transitions of the motivation profiles
- Affordances and limitations of the method in focus
- Conclusion and future directions
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