Analysis of Field Data

The course will review the analytic methods applied in field studies . The course aims to achieve basic knowledge and training in the design and planning of field experiments, the capability to handle statistical analysis, and the graphical presentation of field data with modern statistical software. The course includes project work, in which students will analyze and write a report based on a real dataset. The course is divided into the following modules: Module 1: Theory, 10 ECTS This module is divided into three sections: Section 1: Introduction, 2 ECTS This section introduces the software used throughout the course and covers the basics of designing and planning field experiments, parameter estimation, hypothesis testing, and graphical presentation. Section 2: Analysis, Interpretation, and Presentation of Field Data, 7 ECTS This section addresses how the design, analysis, and interpretation of a study are influenced by the type of data used. It covers the design, analysis, and presentation of experiments, discussing the selection of parameters concerning statistical analysis and the ecological interpretations that can be drawn. The construction of methods based on variance analysis, such as regression analysis, ANOVA, ANCOVA, variance component analysis, and contrasts, are covered. Basic concepts and applications of Generalized Linear Models and Contingency Tables are also included. The use of various types of analyses in scientific articles is critically examined. In computer lab sessions, discussions will cover how to present analyses in scientific articles and how to translate analysis results into relevant interpretations. Section 3: Planning and Analysis of Monitoring Programs, 1 credit This section introduces the planning and analysis of monitoring programs, where students independently design or evaluate monitoring programs through computer-based exercises. **Module 2: Applied Project, 5 ECTS** This module involves an independent analysis of a dataset, allowing students the option to work with their data. In this project, several methods covered earlier in the course will be applied. The dataset should be analyzed with a focus on parameter estimation and statistical significance, and the results should be presented in a report. This module also includes guidelines on presenting data analyses in articles and reports.