| Responsible Department | Department of Basic Science and Environment | ||||||||||||||
| Earliest Possible Year | Post experience Master´s Programme | ||||||||||||||
| Duration | Outside schedule | ||||||||||||||
| Credits | 4 (ECTS) | ||||||||||||||
| Level of Course | Post experience masters programme | ||||||||||||||
| Examination | Final Examination written examination All aids allowed Description of Examination: Individual report on a statistical project must be handed in one month after the week of teaching at LIFE. Weight: Assessment of report 100% 7-point scale, internal examiner Dates of Exam: | ||||||||||||||
| Requirement for Attending Exam | Active participation in discussions of assignments on the web | ||||||||||||||
| Organisation of Teaching | 45 hours e-learning with lectures and exercises, 4 days (March 14-17 2011) at LIFE with 10 hours of lectures and discussions, and 20 hours of exercises and project work. Afterwards 35 hours independent work with exam project. | ||||||||||||||
| Language of Instruction | English | ||||||||||||||
| Course Content | |||||||||||||||
| Classical experimental design: parallel groups, block design, cross-over design; Principles of design: factors, replication, randomization and blocking. Quantitative and categorical data types. Statistical methods: analysis of variance and t-tests, non-parametric alternatives, chi-squared tests, analysis of covariance and factorial models. Dose-response models and regression. Repeated measurements and random effect factors. Power and precision of an experiment. Lectures will use the statisticak program R, but the participant may use his/her favorite statistical software if it does not entail severe limitations. | |||||||||||||||
| Teaching and learning Methods | |||||||||||||||
| First period (from 4 weeks before gathering): e-learrning lectures and exercises based on (mostly) individual study. Second period (4 days gathered at LIFE): lectures and discussions, exercises and minor projects. Third period (the subsequent month): individual work with exam project. | |||||||||||||||
| Learning Outcome | |||||||||||||||
| The course reviews classical types of experimental design together with planning issues like appropriate number of animals related to precision and power, randomization and applicable design types. Most commonly used statistical methods in animal experiments and their pitfalls are presented. Major emphasis is on possibilities of increasing precision via both design and statistical analysis with the aim of minimizing the number of animals needed. At the end of the course the participant should have learned to Knowledge -describe classical types of experimental design used in animal experiments - recognize types of experiments and data calling for advanced statistical methods Skills - plan and substantiate the design of an animal experiment, including issues of randomization, precision and power, - analyse prototypes of such experiments statistically Competences - assess the use of conventional statistical methods for analysis of common types of animal experiments, - interpret and report results of statistical analyses in accordance with statistical thinking, | |||||||||||||||
| Course Literature | |||||||||||||||
| Festing MFW, Overend P, Das RG, Borja MC, Berdoy M: The Design of Animal experiments: reducing the use of animals in research through better experimental design, Laboratory Animals Handbooks No 14 2002 Morris, T.R. (1999). Experimental design and analysis in animal sciences. CABI Pulishing. Both are highly recommended, but none of them required. The first is more verbal and focused, the second is more statistical. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Ib Skovgaard, ims@life.ku.dk, Department of Basic Sciences and Environment/Statistik, Phone: 353-32340 | |||||||||||||||
| Study Board | |||||||||||||||
| Study Committee V | |||||||||||||||
| Work Load | |||||||||||||||
| |||||||||||||||