Applying latent profile analysis in foreign language anxiety research: Uncovering hidden groups
Bibliographic record
Abstract
To gain a deeper understanding of the complexity of Foreign Language Anxiety (FLA), researchers have leveraged various quantitative and qualitative methods. Considering the quantitative methods, researchers have mostly relied on variable-centered approaches to examine the relationships between FLA and other variables. However, less attention has been given to person-centered approaches, which aim to identify subgroups of a population to better understand individual differences and heterogeneity. This study applies latent profile analysis (LPA), a robust person-centered method, to uncover FLA profiles and to examine the predictors and outcomes of FLA profiles. To this aim, we first reviewed person-centered methods, addressing best practices and methodological considerations for conducting LPA. For the empirical study, we gathered data from 384 tertiary-level EFL learners using a questionnaire, which measured their FLA, achievement goals, and willingness to communicate. The LPA results revealed five distinct latent profiles of FLA, characterized not only by the intensity of anxiety but also its manifestations and triggers. Each profile also showed meaningful differences in achievement goals and willingness to communicate. By applying LPA, we could gain a deeper understanding of how FLA is experienced across different learner subgroups. We believe person-centered approaches, such as LPA, provide additional value to investigate anxiety and other emotions in language education research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".