The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review
Bibliographic record
Abstract
The researchers of various disciplines often use qualitative and quantitative research methods and approaches for their studies. Some of these researchers like to be known as qualitative researchers; others like to be regarded as quantitative researchers. The researchers, thus, are sharply polarised; and they involve in a competition of pointing out the benefits of their own preferred methods and approaches. But, both the methods and approaches (qualitative and quantitative) have pros and cons. This study, therefore, aims to discuss the advantages and disadvantages of using qualitative and quantitative research approaches and methods in language testing and assessment research. There is a focus on ethical considerations too. The study found some strengths of using qualitative methods for language “assessment and testing” research—such as, eliciting deeper insights into designing, administering, and interpreting assessment and testing; and exploring test-takers’ behaviour, perceptions, feelings, and understanding. Some weaknesses are, for instance, smaller sample size and time consuming. Quantitative research methods, on the other hand, involve a larger sample, and do not require relatively a longer time for data collection. Some limitations are that quantitative research methods take snapshots of a phenomenon: not in-depth, and overlook test-takers’ and testers’ experiences as well as what they mean by something. Among these two research paradigms, the quantitative one is dominant in the context of language testing and assessment research.
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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 | 0.084 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.035 | 0.037 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".