Integrative Mixed Methods Data Analytic Strategies in Research on School Success in Challenging Circumstances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are both conceptual and practical challenges in dealing with data from mixed methods research studies. There is a need for discussion about various integrative strategies for mixed methods data analyses. This article illustrates integrative analytic strategies for a mixed methods study focusing on improving urban schools facing challenging circumstances. The research was conducted using a concurrent mixed methods approach. The qualitative and quantitative strands of data were analyzed independently through thematic analysis of qualitative data and factor analysis of survey data, followed by integrative data analytic procedures. The integrative data analytic approach included strategies such as parallel integration for member checking, data transformation for comparison, data consolidation for emergent themes, and case analysis for fine-grained descriptions of school profiles. The integrative data analysis process featured the iterative nature of mixing data sources at various points and allowed the researchers to pay attention to emergent insights made available through mixed methods research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.310 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it