Prevalence and Quality of Mixed Methods Research in Educational Subdisciplines: A Systematic Review
Bibliographic record
Abstract
This study examines the prevalence and quality of mixed methods research (MMR) in educational journals, highlighting its growing acceptance yet emphasizing the need for enhanced methodological rigor. Although MMR has become popular across education sub-disciplines, its specific use in educational research is underexplored. This study aims to bridge this gap by investigating MMR prevalence and quality in flagship journals within education sub-disciplines of Leadership, Learning Sciences, Curriculum and Learning, and Adult Learning from 2011 to 2024. A mixed-method systematic review was conducted across nine flagship educational journals. Articles mentioning MMR in the title or abstract were identified, yielding 169 articles, with 132 included after full-text review. Creswell and Plano Clark’s typology classified MMR designs, while the JMMR checklist assessed methodological quality. Quantitative data were analyzed using descriptive statistics and one-way ANOVA, and thematic analysis was applied to qualitative commentary. MMR prevalence in the selected journals rose from 0.64% in 2011 to 2.97% in 2024. Leadership showed the highest prevalence (2.53%), while Curriculum and Learning had the lowest (1.08%). Explanatory sequential design was the most frequently used. The average alignment with the JMMR checklist was low (7.85 out of 20), with significant differences across subdisciplines ( p < .05). The study underscores the growing acceptance of MMR in educational research and highlights the need for comprehensive training in mixed methods research to improve methodological quality. The study is limited by restricted scope of educational subdisciplines and journals.
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
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 | 0.147 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".