Nonparametric Rank Tests for Independence in Opinion Surveys
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Bibliographic record
Abstract
Nonparametric rank tests for independence between two characteristics are commonly used in many social opinion surveys. When both characteristics are ordinal in nature, tests based on rank correlations such as those due to Spearman and Kendall are often used. The case where some ties exist has already been considered whereas Alvo and Cabilio (1995) have studied the case when there are missing values but no ties in the record. However, it frequently happens that the survey data may contain simultaneouslymany tied observations and/or many missing values. A naive approach is to simply discard the missing observations and then to make use of the rank correlations adjusted for ties. This approach would be less powerful as it does not fully utilize the information associated with the incomplete data set. In this article, we generalize Alvo and Cabilio’s notion of distance between two rankings to incorporate tied and missing observations, and define new test statistics based on the Spearman and Kendall rank correlation coefficients.We determine the asymptotic distribution of the Spearman test statistic and compare its efficiency with the corresponding statistic based on the naive approach. The proposed test is then applied to a real data set collected from an opinion survey conducted in Hong Kong.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it