Exploratory and Confirmatory Factor Analyses of L2 Linguistic Complexity Measures
Bibliographic record
Abstract
This study investigated the latent structure of L2 linguistic complexity as a multidimensional construct and analyzed the relationship between the sub-constructs of L2 linguistic complexity by employing exploratory and confirmatory analyses of a set of linguistic complexity measures indexing different sources of L2 linguistic complexity. Based on relevant theories and empirical studies, 11 automated measures indexing distinct sources of syntactic and lexical complexity were selected and used to assess the linguistics complexity of 930 EFL argumentative essays, which were then equally divided into two subsamples. Sample 1 was used for exploratory factor analysis while sample 2 was used for confirmatory factor analysis. The results show that L2 linguistic complexity is a multi-dimensional construct composed of clausal subordination, phrasal elaboration and lexical complexity. Furthermore, regarding the relationships among the three sub-constructs, it was found that lexical complexity and phrasal elaboration are moderately correlated; while clausal subordination employs rather different means of complexification than that employed by phrasal elaboration and lexical complexity. Findings of the study provide empirical evidence for the multidimensionality of L2 linguistic complexity in L2 argumentative writing and lend support to the hypothesis that lexical complexity and grammatical complexity constitute separate, independent dimensions of L2 performance and proficiency, and that there was a certain level of trade-off effect between them.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".