Current Mixed Methods Practices in Qualitative Research: A Content Analysis of Leading Journals
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.
How this classification was reachedexpand
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 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 | 0.246 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.044 | 0.056 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".