Current Mixed Methods Practices in Qualitative Research: A Content Analysis of Leading Journals
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
Mixed methods research (MMR) has become increasingly popular in recent years. Yet, methodological challenges of mixing qualitative and quantitative data remain. Understanding how MMR is approached in qualitative research journals provides insights into lingering mixing issues. In this article, we content analyzed five leading qualitative research journals from 2003 to 2014, which represents the reflective period of MMR. Of the 5,254 articles published, 94, or 1.79%, were mixed methods in nature, comprising 44 theoretically oriented articles and 50 empirical articles. In terms of theoretical articles, five content-based themes were identified: (a) MMR advocacy, (b) philosophy issues, (c) procedural suggestions, (d) practical issues and best practices, and (e) future directions. In terms of empirical articles, 36% used exploratory sequential designs, primarily to develop instruments, and 52% explicitly identified as MMR. None of the studies included MMR questions, and development (21%) and complementarity (14%) were the primary rationales for mixing. In virtually all studies (98%), mixing occurred at the data interpretation stage through some comparison of qualitative and quantitative research. Qualitative data were prioritized in 86% of the studies. Based on these findings, it appears that MMR affects qualitative research most directly by influencing study design and study purpose; however, there is a strong tendency to conduct and publish qualitative and quantitative studies separately. Recommendations for publishing future MMR are discussed.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.415 | 0.304 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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