Using a multitrait‐multimethod analysis to examine conceptual similarities of three self‐regulated learning inventories
Bibliographic record
Abstract
BACKGROUND: A programme of construct validity research is necessary to clarify previous research on self-regulation and to provide a stronger basis for future research. AIM: A multitrait-multimethod (MTMM) analysis was conducted to assess convergent and discriminant validity of three self-regulation measures: the Learning and Study Strategies Inventory (LASSI; Weinstein, 1987), the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1993) and the Meta-cognitive Awareness Inventory (MAI; Schraw & Dennison, 1994). Method bias across all three inventories was also examined. SAMPLE AND METHOD: Three hundred and eighteen undergraduate university students (255 female, 61 male, 2 did not specify) were recruited from various courses to participate in research on perceptions about studying and study methods. Participants spent 30-60 minutes completing all three inventories. RESULTS: Evidence for convergent validity was found at the matrix level, but was attenuated when examined at the individual parameter level. Evidence for discriminant validity among traits was modest, and common method bias was evident across all three measures. CONCLUSIONS: Results revealed the three inventories yielded different results, which suggests that researchers should be selective in the inventory they use to assess self-regulated learning (SRL).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".