Using the Specification Curve to Teach Spatial Data Analysis and Explore Geographic Uncertainties
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| Title: | Using the Specification Curve to Teach Spatial Data Analysis and Explore Geographic Uncertainties |
|---|---|
| Language: | English |
| Authors: | Kedron, Peter (ORCID |
| Source: | Journal of Geography in Higher Education. 2022 46(2):304-314. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2022 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Geography Instruction, Data Analysis, Meta Analysis, Decision Making, Computer Software, Instructional Materials, Teaching Methods, Regression (Statistics), Predictor Variables, Visual Aids, Geographic Information Systems, Information Science, Housing, Goodness of Fit, Costs, Ownership, Generalization, Publications, Bias |
| Geographic Terms: | Maryland (Baltimore) |
| DOI: | 10.1080/03098265.2021.1901076 |
| ISSN: | 0309-8265 1466-1845 |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2022 |
| Accession Number: | EJ1344171 |
| Database: | ERIC |
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| Abstract: | 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. |
|---|---|
| ISSN: | 0309-8265 1466-1845 |
| DOI: | 10.1080/03098265.2021.1901076 |
