Integrative Mixed Method Data Analytic Strategies in Research on School Success in Challenging Circumstances
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 quali- tative data and factor analysis of survey data, followed by integrative data analytic proce- dures. 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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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 | 0.414 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".