A New Look at the Influence of Guessing on the Reliability of Multiple-Choice Tests
Bibliographic record
Abstract
Previous studies have established that chance success due to guessing contributes to error variance and diminishes the reliability of multiple-choice tests and true-false tests. However, the practical usefulness of these theoretical results remains doubtful. Equations that have been derived have not often been used in practical work in testing and test construction. One reason is that relatively little is known about how guessing combines with other sources of error variance that determine test reliability and what proportion of the total variance of test scores is accounted for by guessing. This article derives explicit formulas that allow for combinations of error variance due to guessing and other sources of error. These formulas provide a more realistic guide as to how much improvement in reliability can be expected by altering parameters such as number of test items, number of item choices, and the means and variances of examinees' observed scores.
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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 | 0.036 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".