Bibliographic record
Abstract
This is an assignment for PSYC 6000 Advanced Stats, offered at Memorial University of Newfoundland (MUN) , St. John's, NL, Canada. In this assignment, we will be introduced to the basics of statistics in Psychology. All the data used in this assignment are fictional and are meant for educational purposes only. The assignment revolves around the Big 5 categories of personality and is divided into two main sections. In section 1, we will be using Jamovi for the following: -Reverse coding some data sets (the transform option in Jamovi) -Filtering data from participants above 60 years of age (the filter option in Jamovi) -Conducting and reporting descriptives statistics on the age and gender of participants, using the filtered data -Calculating the mean for each of the Big 5 categories -Provide a visualization for each of the categories and a description for their respective data distribution -Provide a visualization for each of the categories but using age as an independent variable and reporting a description for their respective data distribution In section 2, G*Power software will be used for the following: -Computing the sample size needed for a power of 0.95 to detect a small effect, at an alpha level of .01 -Changing some of the parameters to obtain a smaller sample size and reporting what we did -Provide an explanation on which compromises are required to obtain a smaller required sample size in terms of statistical power in Psychology In this assignment, a fictitious sample of 246 participants (after filtering the false data) was collected and used for this research.
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.
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 | 0.011 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.891 | 0.760 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".