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.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.