Spatial Autocorrelation and Inferential Tests
Bibliographic record
Abstract
The presence of autocorrelation in data violates the usual assumption of independence in the data for evaluating inferential statistics. We describe several models of autocorrelation in spatial data (both positive and negative). Given two serial variables, x and y , autocorrelation observed in y can be due to inherent autoregression in the variable itself, autoregression induced by its dependence on x , which has its own autocorrelation, or doubly autoregressive , with autocorrelation in both variables. This effect can be addressed by estimating the effective sample size (number of independent observations equivalent in information content to the n that are autocorrelated). We present the calculation of the effective sample size for many inferential statistics, including correlation, partial correlation, t -tests and ANOVA. The use of restricted randomization is explained as a method for testing when other approaches are not available. We also provide recommendations for sampling and experimental design in the presence of spatial autocorrelation.
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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 | 0.038 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".