Mixed methods research teams: leveraging integrative teamwork for addressing complex problems
Bibliographic record
Abstract
Mixed methods research teams have garnered increased attention for their leveraging of diverse disciplinary and methodological expertise in pursuit of complex problems. We advance our theoretical viewpoint of integrative mixed methods research teamwork as necessary with empirical evidence demonstrating the equipping mixed methods researchers to study complex problems involving interacting systems and lacking known solutions. Integrative mixed methods research teamwork is distinguishable by the purposeful integration of qualitative and quantitative perspectives to generate novel outcomes that are greater than the sum of individual members’ contributions. Among the key dilemmas faced by mixed methods researchers wanting to work integratively within a team is the lack of practical guidance for how to get started, how to recognize the emergence of synergistic outcomes, and how to sustain a team’s integrative work. To begin addressing this gap, we describe three practical insights gleaned from examining our team interactions and outcomes using a reflection-in-action process during a recent empirical mixed methods case study of literacy practices. In our examination, we test the practical usefulness of a theoretical framework for demystifying the development of a mixed methods research team’s integrative capacity. Our insights contribute to refining teamwork practices by identifying enablers of integrative capacity and proposing ways to overcome hindrances that have not been previously elucidated. We argue that the capacity for integrative teamwork is essential for researchers employing mixed methods, allowing them to leverage inherent synergies when addressing complex problems.
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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 | 0.351 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".