An introduction to retrodictive qualitative modeling as an emerging method on affective variables in SLA research.

Investigating second language acquisition (SLA) via a complex dynamic systems theory (CDST) involves much intuition, and operationalizing the dynamic constructs is hard in research terms. In the present study, we contend that the commonly used quantitative data analysis methods such as correlational works or structural equation modeling fail to examine variables as part of a system or network. They are mostly based on linear rather than non-linear associations. Considering the major challenges of dynamic systems research in SLA, we recommend that innovative analytical models such as retrodictive qualitative modeling (RQM) be used more. RQM manages to reverse the usual direction of research by actually beginning from the end. More especially from certain outcomes and then moves backward to find why specific elements of the system led to one outcome rather than the others. The analytical procedures of RQM will be elaborated on and also exemplified in the SLA research, more specifically for investigating language learners' affective variables. The limited body of research using RQM in the SLA domain is also reviewed followed by some conclusive remarks and suggestions for further research into the variables of interest.

An introduction to process tracing as an innovative qualitative research method to explore affective variables in SLA.

When the Complex Dynamic Systems Theory (CDST) enlightened the line of inquiry in education, innovative research methodologies, both quantitative and qualitative, were also introduced. Process tracing, which is among the CDST-compatible qualitative research methods, has just begun to benefit SLA research in the past few years. The present study provides a review of the conceptualization, significance, and procedural features for the implementation of the process tracing analytical method. In doing so, this review suggests a number of practices through which process tracing has been introduced in SLA. Additionally, some practical implications are provided for SLA researchers to enhance their knowledge of this new approach. Finally, future research suggestions for a more advanced use of this method are made in SLA.

Latent growth curve modeling for the investigation of emotional factors in L2 in longitudinal studies: A conceptual review.

With the advent of Complex dynamic systems theory (CDST) in the field of second language question (SLA), the need for suitable CDST compatible methods for the investigation of temporal change in L2 affective variables has been felt more than before. One of the innovative methods for this purpose is latent growth curve modeling (LGCM), which has recently drawn the attention of SLA scholars. However, the application of this method is still a burgeoning demand in SLA. In response to this demand, the present study provides a review of the conceptualization, significance, and technical features of the implementation of LGCM. In doing so, this review suggests a number of practices via which LGCM has been introduced in SLA. Additionally, some practical implications are provided for SLA researchers to enhance their literacy of LGCM. Finally, future research suggestions for the progress of the use of this method in SLA are discussed.