A Replication of DiMaggio et al. (2020) in Phoenix, AZ

Kedron, Peter; Bardin, Sarah; Hoffman, Tyler; Sachdeva, Mehak; Quick, Matthew; Holler, Joseph

doi:10.1016/j.annepidem.2022.05.005

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Identified errors and uncertainties are presented and discussed in reports. Each of the three reproduction studies is published with open‐source licensing as a reproducible research compendium composed of data, code, pre‐analysis plans and detailed reports of our results (Kedron et al 2022c, d, e). We thereby improve the computational reproducibility of these published studies, provide an enriched assessment of their claims, and facilitate any future research attempting to replicate or extend these studies.…”

Section: Discussionmentioning

confidence: 99%

“…By examining the predictions of central place theory, Davies reanalyzes the data of two studies using slightly different techniques to draw conclusions about the validity of the original analysis and offer possible extensions for future work. A few reproductions by Kedron et al (2022a, b) have brought this approach into the present, but formal, published reproductions and replications that systematically examine the entire research process remain rare in the geographic literature.…”

Section: The Reproduction Of Geographic Researchmentioning

confidence: 99%

“…Geographers have produced similar catalogs of geographical analyses of COVID‐19 but have limited their reviews to listing and categorizing the literature by topical focus and methodological approach (Agbehadji et al 2020; Ahasan et al 2020; Franch‐Pardo et al 2020, 2021). Very few formal reproductions of geographical analyses of COVID‐19 are available in the literature (Paez 2022; Kedron et al 2022a). Conducting reproductions of COVID‐19 research facilitates the assessment of the internal validity of selected studies and demonstrates the value of reproductions.…”

Section: Empirical Context and The Selection Of Studies For Reproductionmentioning

confidence: 99%

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A Framework for Moving Beyond Computational Reproducibility: Lessons from Three Reproductions of Geographical Analyses of COVID‐19

Kedron,

Bardin,

Holler

et al. 2023

Geographical Analysis

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Despite recent calls to make geographical analyses more reproducible, formal attempts to reproduce or replicate published work remain largely absent from the geographic literature. The reproductions of geographic research that do exist typically focus on computational reproducibility—whether results can be recreated using data and code provided by the authors—rather than on evaluating the conclusion and internal validity and evidential value of the original analysis. However, knowing if a study is computationally reproducible is insufficient if the goal of a reproduction is to identify and correct errors in our knowledge. We argue that reproductions of geographic work should focus on assessing whether the findings and claims made in existing empirical studies are well supported by the evidence presented. We aim to facilitate this transition by introducing a model framework for conducting reproduction studies, demonstrating its use, and reporting the findings of three exemplar studies. We present three model reproductions of geographical analyses of COVID‐19 based on a common, open access template. Each reproduction attempt is published as an open access repository, complete with pre‐analysis plan, data, code, and final report. We find each study to be partially reproducible, but moving past computational reproducibility, our assessments reveal conceptual and methodological concerns that raise questions about the predictive value and the magnitude of the associations presented in each study. Collectively, these reproductions and our template materials offer a practical framework others can use to reproduce and replicate empirical spatial analyses and ultimately facilitate the identification and correction of errors in the geographic literature.

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Section: Discussionmentioning

confidence: 99%

Section: The Reproduction Of Geographic Researchmentioning

confidence: 99%