AGRO 896: Applied Field Research: Design, Data Management, Analysis and Reporting

AGRO 896: Applied Field Research: Design, Data Management, Analysis, and Reporting

Instructor(s): Blaine Johnson and Amanda Easterly

Number of Credit Hours: 2 (Eight Week Course)

Prerequisites: STAT 801 or equivalent required; one semester of matrix algebra and/or  one semester of experimental design recommended, but not required.

Description: AGRO 896: Field Research: Design, Analysis, and Reporting (AGRO-896 Independent Study) is a two-hour credit course, offered over eight weeks. The course is an introduction to balanced and unbalanced experimental designs, as used in field crop research, and the use of mixed model methodologies for analysis and summarization of data. This course is designed to provide graduate students with practical knowledge and a working understanding of current statistical methodologies used for designing agricultural field experiments, for data management and organization, and for subsequent analysis, summarization, and reporting of data generated from such experiments. 

Learning Outcomes/Course Objectives

In this course each participant will develop his/her ability to:

  1. Design efficient and practical field experiments that (a) will provide data to answer the biological question that motivated the particular experiment; and (b) do so while satisfying the underlying biological and statistical principles;
  2. Optimize experimental design with available resources and understand advantages/limitations associated with different designs;
  3. Critically plan the flow of data management: the steps of data collection, what types of metadata to record, quality checking and validation of raw data, intermediate analysis, and reporting/summarization of output;
  4. Understand (1) the underlying fundamentals mixed of model methodologies such that the methodology can be confidently used for analysis of field data and (2) the flexibility that mixed model methodology allows when designing field experiments; 
  5. Identify and validate software options needed for executing mixed model analyses;
  6. Execute the analysis of both simple and complex data sets, using field generated data, mixed model methodologies, and appropriate software packages;
  7. Effectively interpret and communicate outputs from analyses;
  8. Most Importantly:  THINK critically and logically!