Statistics and Study Design in Ecology and Conservation
- Importing, manipulating, and exploring datasets utilizing the R computing environment.
- Determining the appropriate type of analysis for a given dataset based on the type of data and whether it meets the assumptions of the chosen analysis.
- Performing parametric, non-parametric, and regression-based analyses, including post-hoc tests.
- Describing study designs appropriate to ecological or conservation questions of interest, and linking study design to the statistical analyses.
- Creating publication-quality figures that effectively portray results of the analyses and help tell the story of your data.
An introduction to coding in R is included in this course: no prior experience in R is expected or required.
- Dates
January 16 – April 29, 2024 | Mason’s Academic Calendar
- Available Formats
Undergraduate (CONS 460, 3 credits)
Graduate (CONS 560, 3 credits)- Cost
- Who is eligible?
Undergraduate or graduate students who have completed an introductory statistics course (BIOL 214, SOCI 313, STATS 250, CONS 404, or equivalent) and students (including non-degree seeking) from any accredited college or university who have completed similar coursework.
Meet the Faculty
Curriculum
An understanding of statistics and study design is essential to success in the fields of ecology and conservation. However, many of the analyses of greatest utility for ecological data are often not addressed in introductory courses, while advanced courses often delve deeply into a limited set of techniques.
This course bridges this gap. Building on knowledge obtained in introductory courses, students will gain familiarity with useful techniques for many forms of ecological data, while setting the stage for success with advanced techniques. This course will address the fundamentals of study design, linking choices made when establishing a research project to the types of analyses appropriate to the chosen design. Emphasis will be placed on understanding the output of analyses, and separating statistical significance from biological or ecological significance. Students will also develop skills in data manipulation and analyses using the R statistical computing environment, which is rapidly becoming the software of choice in both ecology and conservation.
This is an asynchronous online course comprised of five three-week modules. For each module, students are expected to work through a set of instructional videos and associated exercises, as well as complete a problem set. At an optional, virtual weekly meeting, students will be provided an opportunity to discuss and collaborate on the current assignment.
