Intra-Study Matching Considerations When Using Mixed Methods-Based Research Approaches: A Critical Dialectical Pluralistic Approach
Bibliographic record
Abstract
The step of obtaining a sample(s) (i.e., sampling) in mixed methods-based research studies likely represents the least developed step in the research process, with only 21 Scopus-indexed works published on the topic to date. Consequently, the time is rife for mixed methods-based researchers to develop sampling designs that are more TREEful—that is, transparent, rigorous, equitable, and ethical—especially when sampling among/between phases/components. Because, more than the other 13 mixed methods-based research philosophies, critical dialectical pluralism especially is concerned with the welfare of research participants, and because the sampling step is subject to misuse and abuse of participants, the use of a critical dialectical pluralist lens to ensure that mixed methods-based sampling designs are as TREEful as possible has logical appeal. Therefore, in this editorial, we have provided a meta-framework,1 via a critical dialectical pluralism lens, for selecting samples for each of the following four types of relationships among/between phases/components identified by Onwuegbuzie and Collins (2007), namely, identical samples, parallel samples, nested samples, and multilevel samples. This lens has led to the identification of several options for minimizing, or at least reducing, what we refer to as identical sampling bias, parallel sampling bias, nested sampling bias, and multilevel sampling bias such that samples are optimally matched within a single mixed methods-based research study. In the context of mixed methods-based research, matching refers to the process of forming groups to make them as similar as possible with respect to extraneous or confounding factors (e.g., demographic variables [e.g., gender, age]; personality variables [e.g., resilience]; affective variables [e.g., motivation]). In particular, we outline the use of several matching techniques—specifically, exact matching, greedy matching, optimal matching, propensity score matching, subclassification, and magnitude coding—for addressing these different forms of bias. We encourage mixed methods-based researchers to explore using one or more of these matching techniques, whenever appropriate, regardless of their philosophical stance, in order to avoid researcher participants from being misrepresented.
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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 | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.714 | 0.780 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.017 | 0.022 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".