Mixed Methods Research in Library and Information Science: A Methodological Review
Bibliographic record
Abstract
Abstract Objective - To review mixed methods research trends in the field of library and information science (LIS). In particular, we examine the extent to which research about or using mixed methods has been occurring in library and information science over the past decade (2008-2018), and how much of that mixed methods research is done in health contexts. Methods - We conducted a methodological review and analysis of mixed methods research (MMR) in LIS for published articles indexed in LISTA and Web of Science. After deduplication and verification for inclusion, we coded 417 articles to identify contributions using or about MMR. Given the connections between evidence based practice in health and LIS, we also identified whether articles about or using mixed methods were health-focused. Results - We found MMR to be a tiny proportion (less than 0.5%) of the overall LIS research literature. In terms of observable trends, while contributions about MMR remain fairly static, there has been an increase in articles using mixed methods. Of the 417 included articles, 373 (89.5%) primarily used mixed methods and 44 (10.5%) were primarily about MMR. Results also demonstrated that health-related research both using and about mixed methods has a strong presence in the LIS literature, with 136 published articles (32.6% of the total). Conclusion - Confirming findings of prior analyses of research methods in LIS, our methodological review shows current opportunities to adopt and expand the use of mixed methods research processes. Further contributions about mixed methods research, and ideally connecting research and practice in LIS, are needed. Despite the small proportion of MMR in LIS research, there is an observable increase in the number of publications using mixed methods during this timeframe. The LIS research community can promote additional growth by leveraging this momentum around using mixed methods, and look to translate lessons learned about mixed methods research and practice in health contexts to other LIS settings. Recommendations include developing educational opportunities and learning resources that facilitate wider engagement with MMR in LIS contexts.
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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 | 0.264 | 0.472 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".