Method Sequence and Dominance in Mixed Methods Research: A Case Study of the Social Acceptance of Wind Energy Literature
Bibliographic record
Abstract
As more researchers have considered the use of mixed methods, writings have moved away from debates about epistemological incompatibilities and now focus on the (potential) value of increased understanding that comes from combining qualitative and quantitative approaches. Yet, as the level of integration can vary substantially, some designs are said to allow one method or the other to dominate. Although there may be sound reasoning for intentionally allowing one method to dominate, here we investigate one literature as a moment to reflect why, and on the degree to which mixed methods sequence is so bound up with methodological dominance, that calling such studies “mixed” may seem misleading. Like the history of social science more generally, it is quantitative research that is typically given more weight in these studies and academics have noted a few reasons why this may be the case. Few have investigated how research design—and more specifically method sequence—may impact method dominance. Using an emerging mixed methods literature surrounding the social acceptance of wind energy ( N = 34), we study the relationship between the timing of each method (i.e., sequence) and method dominance to see whether qualitative methods in particular are marginalized. Through our Dominance in Mixed Methods Assessment model, we provide evidence that indeed qualitative methods are marginalized and this may be associated with method sequence and other design elements. Moreover, some authors focus solely on one method, giving pause to caution both writers and readers about the use of the term “mixed methods.” The analytical approach is detailed enough to be replicated and detect whether these patterns are repeated in other research domains.
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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 | 0.196 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".