The implications of a mixed methods way of thinking to practice
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
The foremost challenge in mixed methods research is not simply to align data or findings from different analytical procedures but to find an approach that makes it possible to integrate them in an informed way. The article engages more than 40 years of collective conversations with colleagues and students to describe and illustrate a mixed methods way of thinking and the dialectical logic associated with it as one approach to mixed methods research. The article synthesizes more than 20 years of methodological literature to pinpoint five ways that a mixed methods way of thinking has been conceptualized in the literature. This includes as a mindset or stance, an analytic logic that promotes complex thinking, a theoretical framework, a philosophical paradigm that influences collaboration, and a multi-level approach to integrating data and/or findings to produce original insight. Implications for practice are juxtaposed with each of the conceptualizations to consider how a mixed methods way of thinking embodies an inquiry logic that engages complexity, provides an organizing framework to inform design choices, informs establishing a purpose that is committed to understanding diverse perspectives and experiences, contributes a philosophical grounding for productive collaboration, and, finally, provides a logic and rationale for integrating data from diverse sources. The article closes with an example to illustrate that when the different facets of its expression are considered together, a mixed methods way of thinking can provide an organizing framework to guide in the planning, conducting, and reporting of a mixed methods research study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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