Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects

Daniel A. Griffith () and Richard E. Plant
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Daniel A. Griffith: School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Richard E. Plant: Departments of Plant Sciences and Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA

Stats, 2022, vol. 5, issue 4, 1-20

Abstract: Fundamental to most classical data collection sampling theory development is the random drawings assumption requiring that each targeted population member has a known sample selection (i.e., inclusion) probability. Frequently, however, unrestricted random sampling of spatially autocorrelated data is impractical and/or inefficient. Instead, randomly choosing a population subset accounts for its exhibited spatial pattern by utilizing a grid, which often provides improved parameter estimates, such as the geographic landscape mean, at least via its precision. Unfortunately, spatial autocorrelation latent in these data can produce a questionable mean and/or standard error estimate because each sampled population member contains information about its nearby members, a data feature explicitly acknowledged in model-based inference, but ignored in design-based inference. This autocorrelation effect prompted the development of formulae for calculating an effective sample size (i.e., the equivalent number of sample selections from a geographically randomly distributed population that would yield the same sampling error) estimate. Some researchers recently challenged this and other aspects of spatial statistics as being incorrect/invalid/misleading. This paper seeks to address this category of misconceptions, demonstrating that the effective geographic sample size is a valid and useful concept regardless of the inferential basis invoked. Its spatial statistical methodology builds upon the preceding ingredients.

Keywords: design-based; model-based; Monte Carlo simulation; random sampling; spatial autocorrelation; variance inflation (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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