Full TGIF Record # 310247
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Web URL(s):https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/121038
    Last checked: 01/31/2020
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Dumelle, Michael; Mattox, Clint; Braithwaite, Emily T.; McDonald, Brain W.; Kowalewski, Alec
Author Affiliation:Dumelle: Statistics, Oregon State University, Corvallis, OR; Mattox and McDonald: Oregon State University, Corvallis, OR; Braithwaite and Kowalewski: Horticulture, Oregon State University, Corvallis, OR
Title:Adjusting standard ANOVA methods to account for heterogeneous variances with an application to turfgrass management
Section:C05 turfgrass science
Other records with the "C05 turfgrass science" Section

Turfgrass pest management oral II: Diseases, insects, and weeds
Other records with the "Turfgrass pest management oral II: Diseases, insects, and weeds" Section
Meeting Info.:San Antonio, Texas: November 10-13, 2019
Source:ASA, CSSA and SSSA International Annual Meetings. 2019, p. 121038.
# of Pages:1
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
Keywords:TIC Keywords: ANOVA; Analytical methods; Methodology; Models; Variance
Abstract/Contents:"A popular method to analyze the effectiveness of several levels of a treatment on turfgrass health is One-Way Analysis of Variance (One-Way ANOVA). However, ANOVA relies on the assumption of equal variance (homoskedasticity) in the response across all levels of the treatment in question. Unfortunately, this is not always justified in practice. If standard ANOVA is used to analyze data with unequal variance (heteroskedasticity), the resulting p-values can be very inaccurate, giving misleading inference and potentially leading to ill-advised policy decisions. We discuss adjustments to ANOVA to accommodate heteroscedastic data and present positives and negatives of these approaches. We implement these methods on a real data set corresponding to an experiment carried out during 2016 in Western Oregon. The experiment is a randomized complete block design which explores the use of several fungicides to control the health of putting green surfaces. It did not meet the assumptions of heterogenous variance, as several of the treatments exhibited differing levels of variability. We fit a standard ANOVA model to the data as well as other ANOVA models that adjust for heteroskedasticity. We outline similarities and differences between the model fits with respect to inference, prediction, and diagnostic checks. Lastly, we discuss how the methods to adjust ANOVA in order to accommodate heteroskedasticity can be extended to ANOVA settings with multiple factors (Two-Way ANOVA, Three-Way ANOVA, etc.)."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Dumelle, M., C. Mattox, E. T. Braithwaite, B. W. McDonald, and A. Kowalewski. 2019. Adjusting standard ANOVA methods to account for heterogeneous variances with an application to turfgrass management. Agron. Abr. p. 121038.
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    Last checked: 01/31/2020
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