Practical strategies to identify and address discordant findings in mixed methods research
Bibliographic record
Abstract
Integration of qualitative and quantitative data in mixed methods research generates confirmed, discordant, and expanded findings. Guidance is available about the methods and strategies for the meaningful integration of data. However, little has been written about strategies for managing discordant findings in mixed methods. This paper describes and illustrates practical strategies for managing and integrating discordant findings in mixed methods analyses based on researchers reflections and experiences of managing and integrating discordant findings in convergent and sequential exploratory mixed methods studies. Two strategies, namely, comprehensive case and variable analysis and sociocultural exploration are proposed. Comprehensive case analysis involves identifying discordant findings in quantitative data, identifying supportive data in qualitative data, and selecting variables for mixed analysis and interpretation. Sociocultural exploration comprises qualitative code and quantitative data matrix for themes, identification of discordant findings under each theme, and development of sociocultural profile. Identifying and addressing discordant findings in mixed methods is an essential step of rigorous mixed methods analysis. The comprehensive case and variable analysis and sociocultural exploration strategies emphasize the need to examine discordance in data at an early stage of analysis. Further use and evaluation of these strategies are warranted to expand the body of knowledge about practical methods of mixed methods data analysis.
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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 | 0.599 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.006 |
| 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".