A World of Possibilities in Mixed Methods: Review of the Combinations of Strategies Used to Integrate Qualitative and Quantitative Phases, Results and Data
Bibliographic record
Abstract
Mixed methods (MM) are increasingly popular. Researchers integrate qualitative (QUAL) and quantitative (QUAN) methods (e.g., research questions, data collections and analyses, and results). Several integration strategies have been proposed, but their conceptualization is usually design-driven, or fragmented, or not empirically tested. This is challenging for planning and conducting MM studies, and for training graduate students. Based on the methodological literature, we developed a conceptual framework including types of integration and practical strategies, and possible combinations. Then, we tested this framework using 93 health-related 2015 MM studies with a method-detailed description, which illustrated all types of combinations. Our work contributes to advance methodological knowledge on MM via (a) a call for better reporting health-related MM studies, and (b) a tested conceptualisation comprising 3 types of integration and 9 specific strategies, which explain current and future possibilities for combining strategies to integrate QUAL and QUAN phases, results, and data.
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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 | 0.355 | 0.402 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".