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Research Methodology I

Course developers

Course abstract

The research methodology provides basic knowledge and understanding of various empirical studies of the forest ecosystem, their planning and execution, and the suitability of experimental designs for solving specific scientific questions as well as structure and development of master thesis and scientific articles.

Learning outcomes and their assessment

1. The student has an understanding of the advantages and disadvantages of different research designs.
2. The student is able to create the structure of the project (research plan), to prepare and justify the experimental design to solve specific research question.
3. The student knows the main methods of empirical data processing.
4. The student is able to choose the most suitable mathematical methods for the specifics of data using logical analysis, as well as is competent to apply the methods in analysis of empirical data, solving research tasks in forest science.
Learning outcomes are assessed based on the questions in laboratory works (assessment: passed / failed) and tests (assessed in 10 grade scale).

Course Content(Calendar)

1. Basic information (title, aim, tasks) and structure of the master thesis. Analysis of examples and common mistakes (2 h).
2. Statistical sets. The problem of representativeness (2 h).
3. Empirical and theoretical distributions (2 h).
4. Types of research. Structure of research work (2 h)
5. Planning and implementation of experiment, its results and their interpretation: examples (seminar) (16 h).
6. Types of scientific articles. Content and tasks of separate sections of scientific article (2 h).
7. Interpretation of results and discussion. Conclusions (2 h).
8. Primary and secondary sources of scientific literature. Databases of scientific literature (2 h).
9. Plagiarism: how to avoid it. References in the text. List of references (2 h).
10. Selection of appropriate data processing method (2 h).
11. Descriptive statistics: parameters, methods, implementation possibilities (2 h laboratory work).
12. Hypothesis testing. Null hypothesis (2 h laboratory work).
13. Parametric and non-parametric methods (2 h laboratory work).
14. Single factor and multiple strategy. Nonlinearity. (2 h laboratory work).
Test.
15. Analysis of variance (ANOVA). Interdependence of traits. (2 h lectures. 2 h laboratory work).
16. Correlation analysis. Regression analysis (4 h lectures. 4 h - 2 laboratory works).
17. Multiparameter classification (2 h lectures. 2 h laboratory work).
Test
18. Analysis of individual tasks: master thesis essay (practical work) (6 h).

Requirements for awarding credit points

The structure and sections of your research (master thesis) should be prepared: aim, tasks, materials and methods. The seminar and practical works must be attended and 2 tests passed. 7 laboratory works must be completed and defended (assessment: passed / failed). In the end, an accumulative test with a grade, a grade consists of marks from tests and an evaluation of the developed part of student’s research.
Missed laboratory works and seminars as well as failed tests must be rewritten in accordance to procedure set by the Department of Silviculture.

Description of the organization and tasks of students’ independent work

Systematic theory studies, tests and laboratory work. Preparation for seminars and group works, preparation and presentation of individual assignments and resulting work - sections of own research.

Criteria for Evaluating Learning Outcomes

Knowledge, skills and competence are assessed on a 10-point scale. An oral or written answer is successful if at least 50% of the questions are answered correctly.
The tests are evaluated according to the set procedure - after the answers to the questions given in the methodological descriptions at the beginning of each study period, the individual assignments are evaluated based how competently the student is able to apply the acquired theoretical knowledge and skills.

Compulsory reading

1. Pradip Kumar Sahu (2013) Research Methodology: A Guide for Researchers In Agricultural Science, Social Science and Other Related Fields. Springer.
2. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību centrs, 2003. 352 lpp.
3. Levine D. M., Ramsey P.P., Smitd R.K. Applied statistics for Engineers and Scientists: Using Microsoft Excel and MINITAB. Upper Saddle River, New Jersey: Prentice Hall, 2001. 671 p.

Further reading

1. Johnson R.A., Wichern D.W. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River: Pearson Education, Inc., 2007. 773 p.
2. Zar J.H. Biostatistical Analysis. 4th edition. Upper Saddle River: Prentice Hall, 1999. 1996. 663 p.
3. Everitt B.S., Der A. A handbook of statistical analysis using SAS. London: Chapman & Hall, 1996. 157 p.

Periodicals and other sources

1. Biometrics. Journal of the International Biometric Society. ISSN 1541-0420.
2. Nature. Pieejams: https://www.nature.com/
3. European Journal of Forest Research. Pieejams: https://www.springer.com/journal/10342/

Notes

The study course is included in the section of optional study courses of the Master's study program in Forestry science