Design and Analysis of Ecological Research
Surveys major study designs and analysis methods for research, with a focus on biology and environmental science although relevant to other fields. Topics may include data distributions; multivariate regression; single factor and multi-factor analysis of variance; analysis of covariance; random effects and linear mixed effects models; blocked, split-unit, repeated measures, and longitudinal study designs; maximum likelihood and generalized linear models; generalized linear mixed models; generalized additive models; model selection; and causal reasoning. Problem sets use R for real and simulated examples. Students will use mathematical reasoning but not derive results and will practice writing interpretations of statistical results.
Topics
- Students who complete this course will have useful, practical research skills. They will understand tradeoffs in study design such as between different levels of replication, whether to cross multiple treatments, whether to use split plots, whether to block, and how to make appropriate controls. They will know how to analyze these and other common kinds of study designs, which provides a basis for reasoning about design tradeoffs and statistical power. They will know how to use simulations to plan sample sizes and allocation of effort. They will understand what questions can be asked with different designs. They will understand the unification of general, generalized, and mixed effects linear models, as well as related methods based on least squares and maximum likelihood estimation. They will be able to explain and interpret statistical models and results.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
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Sources
- Course catalogue ↗2026-2027