GRD: An SPSS extension command for generating random data
Bibliographic record
Abstract
To master statistics and data analysis tools, it is necessary to understand a number of concepts, many of which are quite abstract. For example, sampling from a theoretical distribution can help individuals explore and understand randomness. Sampling can also be used to build exercises aimed to help students master statistics. Here, we present GRD (Generator of Random Data), an extension command for SPSS (version 17 and above). With GRD, it is possible to get random data from a given distribution. In its simplest use, GRD will return a set of simulated data from a normal distribution. With subcommands to GRD, it is possible to get data from multiple groups, over multiple repeated measures, and with desired effect sizes. Group sizes can be equal or unequal. With further subcommands, it is possible to sample from any theoretical population, (not simply the normal distribution), introduce non-homogeneous variances, fix or randomize subject effects, etc. Finally, GRD's generated data are in a format ready to be analyzed.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.175 | 0.076 |
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".