Using the specification curve to teach spatial data analysis and explore geographic uncertainties
Bibliographic Data
| ID | 11953764 |
|---|---|
| Authors | Peter Kedron (0000-0002-1093-3416, Arizona State University, corresponding author), Matthew Quick (0000-0002-1112-9323, Arizona State University), Zach Hilgendorf (0000-0002-0438-6516, Arizona State University), Mehak Sachdeva (0000-0003-4375-5422, Arizona State University) |
| Year | 2021 |
| Volume | 46 |
| Issue | 2 |
| Pages | 304-314 |
| Publication date | 2021-03-15 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Geography in Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 0309-8265 • E-ISSN: 1466-1845 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/03098265.2021.1901076 |
| OpenAlex | W3138874892 |
| Language | EN |
| Citations received | 3 |
| References cited | 23 |
Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating analyses to obtain results that meet the criteria thought to be required for publication, and the disproportionate reporting of false-positive results in the academic literature. This article proposes that the specification curve – a meta-analytical technique that visualizes the specifications and results from a large set of justifiable and plausible statistical models – be used as a pedagogical tool to teach (spatial) data analysis and explore the geographic uncertainties that arise when specifying and interpreting spatial regression models. An example specification curve that focuses on two common specification decisions in a spatial regression model, specifically selecting predictor variables and constructing the spatial weight matrix, is illustrated. Strategies for using the specification curve in educational contexts to develop analytical plans, reflect on the generalizability of research findings, and highlight issues of replicability and publication bias are proposed
Data mining · Data science · Data set · Econometrics · Generalizability theory · Machine learning · Process (computing · Programming language · Regression analysis · Set (abstract data type · Spatial analysis · Specification · Statistics · Computer Science · Economic and Environmental Valuation · Mathematics · Spatial and Panel Data Analysis · Urban Transport and Accessibility · Artificial Intelligence
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AIC model selection using Akaike weights
The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant
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The future role of GIS education in creating critical spatial thinkers
Teaching mathematics in geography degrees
Antifragile
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| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |