Establishing the Reliability and Validity of Web-based Singing Research
Bibliographic record
Abstract
In this study, the robustness of an online tool for objectively assessing singing ability was examined by: (1) determining the internal consistency and test-retest reliability of the tool; (2) comparing the task performance of web-based participants (n = 285) with a group (n = 52) completing the tool in a controlled laboratory setting, and then determining the convergent validity between settings, and (3) comparing participants’ task performance with previous research using similar singing tasks and populations. Results indicated that the online singing tool exhibited high internal consistency (Cronbach’s alpha = .92), and moderate-to-high test-retest reliabilities (.65–.80) across an average 4.5-year-span. Task performance for web- and laboratory-based participants (n = 82) matched on age, sex, and music training were not significantly different. Moderate-to-large correlations (|r| =.31–.59) were found between self-rated singing ability and the various singing tasks, supporting convergent validity. Finally, task performance of the web-based sample was not significantly different to previously reported findings. Overall the findings support the robustness of the online tool for objectively measuring singing pitch accuracy beyond a controlled laboratory environment and its potential application in large-scale investigations of singing and music ability.
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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 | 0.029 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".