Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Stats |
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| Online Access: | https://www.mdpi.com/2571-905X/5/4/81 |
| _version_ | 1797455168077299712 |
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| author | Daniel A. Griffith Richard E. Plant |
| author_facet | Daniel A. Griffith Richard E. Plant |
| author_sort | Daniel A. Griffith |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:50:32Z |
| format | Article |
| id | doaj.art-5aee874984b745669f381fa5d88dc600 |
| institution | Directory Open Access Journal |
| issn | 2571-905X |
| language | English |
| last_indexed | 2024-03-09T15:50:32Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj.art-5aee874984b745669f381fa5d88dc6002023-11-24T18:04:56ZengMDPI AGStats2571-905X2022-12-01541334135310.3390/stats5040081Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy EffectsDaniel A. Griffith0Richard E. Plant1School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USADepartments of Plant Sciences and Biological and Agricultural Engineering, University of California, Davis, CA 95616, USAFundamental 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.https://www.mdpi.com/2571-905X/5/4/81design-basedmodel-basedMonte Carlo simulationrandom samplingspatial autocorrelationvariance inflation |
| spellingShingle | Daniel A. Griffith Richard E. Plant Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects Stats design-based model-based Monte Carlo simulation random sampling spatial autocorrelation variance inflation |
| title | Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects |
| title_full | Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects |
| title_fullStr | Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects |
| title_full_unstemmed | Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects |
| title_short | Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects |
| title_sort | statistical analysis in the presence of spatial autocorrelation selected sampling strategy effects |
| topic | design-based model-based Monte Carlo simulation random sampling spatial autocorrelation variance inflation |
| url | https://www.mdpi.com/2571-905X/5/4/81 |
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