Spatial analysis for psychologists: How to use individual-level data for research at the geographically aggregated level.
Bibliographic record
Abstract
Psychologists have become increasingly interested in the geographical organization of psychological phenomena. Such studies typically seek to identify geographical variation in psychological characteristics and examine the causes and consequences of that variation. Geo-psychological research offers unique advantages, such as a wide variety of easily obtainable behavioral outcomes. However, studies at the geographically aggregate level also come with unique challenges that require psychologists to work with unfamiliar data formats, sources, measures, and statistical problems. The present article aims to present psychologists with a methodological roadmap that equips them with basic analytical techniques for geographical analysis. Across five sections, we provide a step-by-step tutorial and walk readers through a full geo-psychological research project. We provide guidance for (a) choosing an appropriate geographical level and aggregating individual data, (b) spatializing data and mapping geographical distributions, (c) creating and managing spatial weights matrices, (d) assessing geographical clustering and identifying distributional patterns, and (e) regressing spatial data using spatial regression models. Throughout the tutorial, we alternate between explanatory sections that feature in-depth background information and hands-on sections that use real data to demonstrate the practical implementation of each step in R. The full R code and all data used in this demonstration are available from the OSF project page accompanying this article. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.047 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".