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| Web URL(s): | https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/141737 Last checked: 01/26/2023 Requires: JavaScript; HTML5 |
| Publication Type: | Report |
| Content Type: | Abstract or Summary only |
| Author(s): | Qu, Henry; Kne, Len; Watkins, Eric; Graham, Steve; Morris, Kevin N. |
| Author Affiliation: | Qu: Presenting Author and National Turfgrass Evaluation Program; Kne: University of Minnesota; Watkins: University of Minnesota-Twin Cities; Graham: University of Minnesota Duluth; Morris: National Turfgrass Evaluation Program |
| Title: | A latent scale model to minimize subjectivity for visual rating data in the National Turfgrass Evaluation Program |
| Section: | Turfgrass science oral II Other records with the "Turfgrass science oral II" Section C05 turfgrass science Other records with the "C05 turfgrass science" Section |
| Meeting Info.: | Baltimore, Maryland: November 6-9, 2022 |
| Source: | ASA, CSSA, SSSA International Annual Meeting. 2022, p. 141737. |
| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
| # of Pages: | 1 |
| Abstract/Contents: | "The National Turfgrass Evaluation Program (NTEP) is an internationally renowned turfgrass research program. The traditional evaluation procedure in NTEP relies on the visual assessment of replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups. In this project, we briefly reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity that not only provides objective comparisons of varieties across different subjects and research groups but also allows better monitoring and analysis of the current evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings in the 2017 NTEP Kentucky bluegrass trials at seven testing locations." |
| Language: | English |
| References: | 0 |
| See Also: | See also related item, A Latent Scale Model to Minimize Subjectivity for the Analysis of Visual Rating Data in the National Turfgrass Evaluation Program (NTEP), 2022, R=344319.R=344319 See also related item "A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program" Frontiers in Plant Science, Vol. 14, 2023, p. 1135918 [1-9], R=336957.R=336957 |
| Note: | "309-1" This item is an abstract only! |
| ASA/CSSA/SSSA Citation (Crop Science-like – may be incomplete): | Qu, H., L. Kne, E. Watkins, S. Graham, and K. N. Morris. 2022. A latent scale model to minimize subjectivity for visual rating data in the National Turfgrass Evaluation Program. Agron. Abr. p. 141737. |
| Fastlink to access this record outside TGIF: | http://tic.msu.edu/tgif/flink/RECNO/324971 |
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| Web URL(s) : | https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/141737 Last checked: 01/26/2023 Requires: JavaScript; HTML5 |
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