LV EN RU DE FR

Research Methodology and Biostatistic

Course developers

Prior knowledge

Biol3014, Plant Physiology I

Vete2022, Animal Physiology

Course abstract

The aim of the course is to provide students with the knowledge and skills necessary to design experiments, process and analyse experimental data, and effectively present research results. Emphasis is placed on applying statistical methods and software tools to solve real-world agricultural problems, enabling students to integrate theoretical and practical knowledge in research projects and their bachelor’s thesis. Students acquire experimental design and experimental data processing methods, as well presentation of the results in study works. During the studies the real examples related to agriculture are using. Statistical software is provides for problem tasks solving. Students will use theoretical and practical knowledge in research projects and bachelor work.

Learning outcomes and their assessment

After completing the course student:
Knows and understands the methods of experimental design and data processing, knows the structure of study papers and the bachelor’s thesis (practical assignments completed, written tests successfully passed, theoretical examination successfully completed);
Can choose data processing methods appropriate to the research and the formulated hypothesis, can interpret the obtained results and can formulate conclusions (practical assignments completed, written tests successfully passed, homework successfully completed);
Is able to, in cooperation with the supervisor, carry out bachelor’s thesis research, apply data processing methods, evaluate the obtained results, and compile study results in tables and figures in study papers (written tests successfully passed, independent homework completed and defended).

Course Content(Calendar)

1. Introduction to research design. [L- 2h]
2. The scientific methods. [L- 2h/ P – 2h]
3. Experiment model design for field and livestock research. [L- 2h/ P – 2h]
4. Planning of experimental study. Tools of research. [L- 2h/ P – 2h]
1st test: Research design and methodology. [P – 2h]
5. Descriptive statistics. Presentation of descriptive statistical parameters in study works. [L- 2h/ P – 2h]
6. Correlation analysis. [L- 2h/ P – 2h]
7. Regression analysis. [L- 2h/ P – 2h]
8. Hypothesis testing. Null and alternative hypothesis. Research and statistical hypothesis. [L- 2h]
9. Two paired samples – t-test. [L- 2h/ P – 2h]
10. Two independent samples – F-test and t-test. [L- 2h/ P – 2h]
11. One-way ANOVA. [L- 2h/ P – 2h]
12. Two way ANOVA without and with replication. [L- 2h/ P – 2h]
2nd test: Descriptive statistics, ANOVA, correlation and regression analysis. [P – 2h]
13. Structure of study projects. [L- 1h/ P – 3h]
14. Literature review and list of bibliography sources. [L- 3h/ P – 1h]
15. Study results formatting and presentation in tables and graphs. [L- 1h/ P – 3h]
16. Study results and discussion, conclusions. [L- 3h/ P – 1h]
Homework presentation.

Requirements for awarding credit points

Examination. Examination include a practical tasks on the course subjects and a theoretical subjects acquired during the study course.
All practical works and two tests should be executed before examination. Homework has been developed and public defended.

Description of the organization and tasks of students’ independent work

The organization of independent work during the semester is independently studying literature, using academic staff member consultations. Homework has been developed and public defended. Homework - 2-3 pages: to formulate the aim and two tasks, test the hypotheses using at least 2 statistical methods, based on the experimental data. Complete tables or graphs from the results, write conclusions.

Criteria for Evaluating Learning Outcomes

Exam evaluation depends of the semester cumulative assessment: 1st test – 20%, 2nd test – 20%, homework – 20%, and written examination during the session – 40%. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (at least 4) hold the complex exam during the session. The exam includes practical part (60%) and theory (40%).

Compulsory reading

1. Marczyk G., DeMatteo D., Festinger D. Essentials of Research Design and Methodology. Hoboken, New Jersey: , John Wiley & Sons, Inc., 2005. 305 p.
2. Mead R., Curnow R.N. and Hasted A.M. Statistical methods in agriculture and experimental biology. 2nd ed. London etc.: Chapman and Hall, 1996. 415 p.
3. Gustavii B. How to write and illustrate scientific papaers. Cambridge: Cambridge University Press, 2008. 168 p.
4. Kristapsone S. Statistiskās analīzes metodes pētījumā. Rīga, 2019.
5.Orlovska A., Jurgelāne-Kaldava I. Ekonomiskā statistika. Teorija, piemēri, uzdevumi. – Rīga: RTU Izdevniecība, 2024. 174 lpp.

Further reading

1. Handbook of data analysis. Edited by M. Hardy, A. Bryman. London ... [etc.]: Sage, 2004. 704 p.
2. Beauchamp T. L. and Frey R.G. The Oxford handbook of animal ethics. Oxford, New York: Oxford University Press, 2011. 982 p.
3. Petersen R. G. Agricultural Field Experiments Designe and Analysis. Oregone State Universit, 1994. 409 p. https://www.taylorfrancis.com/books/9780429078491

Periodicals and other sources

1. Animal Frontiers: the review magazine of animal Agriculture. American Society of Animal Science. ISSN: 2160-6064

Notes

Obligatory course for BSc study programme “Sustainable Agriculture”