Twoway Analysis of Variance (ANOVA)

Thomas W. MacFarland and Jan M. Yates
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Thomas W. MacFarland: Nova Southeastern University Fort Lauderdale, Senior Research Associate, Office of Institutional Effectiveness
Jan M. Yates: Nova Southeastern University Fort Lauderdale, Professor Emerita, Abraham S. Fischler College of Education

Chapter Chapter 6 in Using R for Biostatistics, 2021, pp 361-426 from Springer

Abstract: Abstract The purpose of this lesson on Twoway Analysis of Variance (ANOVA) is to provide guidance on how R can be used to see if two or more group means (e.g., Mean of High-Density Lipoprotein (HDL) Cholesterol for Gender breakouts (e.g., Female and Male) and Race-Ethnicity breakouts (e.g., Asian, Black, Hispanic, Indigenous, Unidentified, and White); Mean Degree of Marbling for Breed breakouts (e.g., Hereford, Limousin, and Simmental) and Feeding Program Breakouts (e.g., Pasture with Supplemental Grain and Pasture Only); Mean of Soybean Yield for Tillage Program (e.g., No-Till and Conventional) and Seasonal Rainfall (e.g., Dry, Normal, Wet), etc.) differ due to chance, or if observed differences are indeed the result of true difference and/or interactions between phenomena. Specifically, the Twoway ANOVA statistical test has been structured to examine differences and possible interactions when multiple variables have two or more separate categories. The degree of complexity supported by Twoway ANOVA begins to model real-world concerns for the many ways variables, and possible interaction between and among variables, impact outcomes.

Keywords: ANOVA; Analysis of variance; Balanced design; Between-subjects factors; Dependent variable; Factorial ANOVA; Factorial design; Fisher (Ronald); Independent variable; Interaction; Mean comparison technique; Oneway ANOVA; Predictor variable; Twoway ANOVA; Unbalanced design; Within-subjects factors (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-62404-0_6

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