Time Schedule:
Julian D. Olden
FISH 507
Seattle Campus
Recommended: permission of instructor.
Class description
With recent advances in data collection technology and ambitious field research, ecologists are increasingly required to use multivariate statistics (describing the collection of procedures involving the observation and analysis of two or more dependent variables) to explore and test for patterns in their data. The goal of this course is to introduce upper-level students in the ecological sciences to the multivariate statistical techniques necessary to carry out sophisticated analyses and evaluate the literature. This is a practical, hands-on course emphasizing the analysis and interpretation of multivariate analysis, and covers the majority of approaches in common use by ecologists. The emphasis of the course is on the conceptual understanding and practical use of the methods, with the singular hope of de-mystifying the "alphabet soup" of multivariate analysis.
Student learning goals
An introduction to the use of multivariate statistics in ecological research.
A conceptual organization of the various multivariate techniques, with respect to the types of research questions and data sets appropriate for each technique.
A working understanding of how to use and interpret the results of each technique, including, for each technique, a conceptual overview, list of assumptions, diagnostics for assessing the assumptions, mechanics of performing the analysis using a variety of software, and how to interpret the statistical output of the analysis.
General method of instruction
Lectures/labs: Lectures will integrate both theoretical aspects of specific multivariate techniques and concomitantly provide solutions and interpretations from various software packages. For each topic there will be a formal lecture followed by a computer-based lab where software will be used to analysis ecological data using the particular multivariate technique. The software reviewed will be expansive (minimally: CANOCO, MVSP, PC-ORD, NT-SYS, R-package, PRIMER), but will focus on the use of PC-ORD. The student is not necessarily required to acquire or learn the intricacies of all of these packages, just be aware of the opportunities.
Final report: A significant portion of your grade is based on a final written report. The final project will consist of a statistical analysis of a multivariate data set and will result in a written report. The nature of the question, the source of the data, and the kinds of analysis employed are flexible. The primary requirement is that the data and analysis must address one or more specific biological hypotheses, which are to be tested using an appropriate method of analysis. The primary goal is logical clarity, not number crunching.
Recommended preparation
There is no required text for this course. Statistical texts that are likely to be helpful include:
Legendre, P., and L. Legendre. 1998. Numerical Ecology. 2nd edn. Elsevier Scientific. Digby, P.G.N. and R.A. Kempton. 1987. Multivariate Analysis of Ecological Communities. Chapman & Hall. Gauch, H.G. 1982. Multivariate Analysis in Community Ecology. Cambridge University Press. Manly, B.F.J. 2004. Multivariate Statistical Methods: a primer. Chapman & Hall. McCune, B., and J.B. Grace. 2002. Analysis of Ecological Communities. MJM Press. Jongman, R.H.G., C.J.F. ter Braak, and O.F.R. van Tongeren. 1995. Data analysis in Community and Landscape Ecology. Cambridge University Press.
Class assignments and grading
DETAILS FOR FINAL REPORT Data set: The data set may be your own, one obtained from the literature or one provided by the Instructor. The only requirements are that it be adequate and sufficiently large to test the hypothesis in question.
Analysis: Methods of analysis should be chosen to be compatible with hypotheses and data. Procedures can be either exploratory or inferential.
Report: The report should be written in standard scientific format (i.e., with abstract, introduction, methods, results, and discussion), as if it were to be submitted to a journal of your choice as a paper or note. It should include the following elements: (a) a short, general description of the problem, a paragraph or two in length, with enough background information to allow me to understand what question you're asking, why you're asking it, and why you find it interesting; (b) explicit statements of null and research hypotheses, both at the level of the biological problem and at the level of the test statistic; (c) justification of methods of analysis chosen; (d) a list of the assumptions underlying the study, both biological and statistical, and an assessment of the importance of each assumption in strengthening or weakening the final conclusions; (e) a description of the analytic procedures (methods); (f) tabular summaries of results, including descriptive statistics, test statistics, degrees of freedom, probabilities, and assessments of statistical significance; (g) conclusions, including a discussion of how the assumptions affect your confidence in the results.
Participation in lecture and lab – 20% Report prospectus – 10% Final report – 70%
