Classroom Climate and Contextual Effects: Conceptual and Methodological Issues in the Evaluation of Group-Level Effects
Bibliographic record
Abstract
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes—inappropriately—represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables; use of classroom L2 rather than student-level L1 measures) and more appropriate multilevel models. To illustrate these issues, we consider the effects of two L2 classroom climate variables and one L2 classroom contextual variable on two L1 student-level outcomes for 2261 students in 128 classes. Through this example, we illustrate how to apply evolving doubly latent multilevel models to (a) evaluate the factor structure of L1 and L2 constructs based on multiple indicators of classroom climate and context measures, (b) control measurement error at L1 and L2, (c) control sampling error in the aggregation of L1 responses to form L2 constructs (the average of student-level responses to form classroom-level constructs), and (d) provide guidelines for appropriate analysis of classroom climate as an L2 construct. [Supplementary materials are available for this article. Go to the publisher's online edition of Educational Psychologist for the following free supplemental resources: Substantive basis of the present investigation and more detailed description of the methodology.]
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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 | 0.389 | 0.656 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".