Modelling Count Data in Psychological Research: An Applied Tutorial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across subfields of psychology, researchers frequently encounter count variables (i.e., non-negative integer values, which result from counted measurements). Although count variables are common in psychological research (e.g., frequency of behaviours or symptoms), researchers may not be aware of appropriate statistical procedures for modelling and drawing inferences from count data. Specialised regression techniques (i.e., generalised linear models and zero-augmented models) have been developed for the unique properties of count data, but they can seem inaccessible to non-technical audiences because of their departure from more familiar methods. Assuming a basic knowledge of linear regression, this tutorial aims to demystify count regression approaches and empower researchers to apply these methods to their own count data, using free, open-source statistical software (i.e., R). This tutorial takes researchers step-by-step through the implementation of count regression methods in applied research, imparting them with the knowledge to confidently implement these techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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