Combining the Power of Stories and the Power of Numbers: Mixed Methods Research and Mixed Studies Reviews
Bibliographic record
Abstract
This article provides an overview of mixed methods research and mixed studies reviews. These two approaches are used to combine the strengths of quantitative and qualitative methods and to compensate for their respective limitations. This article is structured in three main parts. First, the epistemological background for mixed methods will be presented. Afterward, we present the main types of mixed methods research designs and techniques as well as guidance for planning, conducting, and appraising mixed methods research. In the last part, we describe the main types of mixed studies reviews and provide a tool kit and examples. Future research needs to offer guidance for assessing mixed methods research and reporting mixed studies reviews, among other challenges.
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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 | 0.460 | 0.674 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.008 | 0.008 |
| 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; 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".