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Research Methodology in Forestry

Course developers

Course abstract

The aim of the study course is to provide basic knowledge and understanding of the principles of choosing methods for processing empirical research data of the forest ecosystem, their planning and execution, the suitability of experimental designs for solving specific scientific issues, as well as the structure and development of master's thesis and scientific articles. Students get acquainted with the verification of the execution of assumptions and the interpretation of the results. Students learn the most widely used methods of mathematical processing of data for solving a specific research issue in the preparation of a master's thesis or publication, using the most appropriate computer programs for statistical analysis.

Learning outcomes and their assessment

The student understands the advantages and disadvantages of various research designs.
Knows how to develop a research structure, prepare and justify the design of an experiment for conducting a particular study.
The student knows the main methods of processing empirical data.
Using logical analysis, he can choose the most suitable mathematical methods for the specifics of data, as well as is competent in the application of the basic methods of empirical data processing and their practical implementation, solving research tasks in forestry science.
The results of the course are evaluated using control questions in laboratory work (score credited/unaccounted for), as well as in tests (assessment on a 10-point scale). Upon successful completion of this course, the student must:
• In-depth knowledge of current scientific theories, insights of data processing methods and their application in research related to the professional field (during practical classes, the necessary data processing methods for testing the research hypothesis have been analyzed and evaluated);
• Skills to independently evaluate and choose appropriate data processing methods, implementing original research of the appropriate amount (the necessary data processing methods for the analysis of research data have been selected during practical classes);
• Competencies in cooperation with the supervisor to carry out independent, critical analysis and evaluation, solve significant research or innovation tasks, independently put forward a research idea (developed and defended homework).

Course Content(Calendar)

1. Plagiarism: how to avoid it. Basic information (title, aim, tasks) and structure of the master thesis (1 h).
2. References in the text. List of references. Analysis of examples and common mistakes (1 h).
3. Master thesis. Methodology. (1h)
4. Master thesis. Results and analyse/discussion. (1h)
5. Interpretation of results and discussion. Conclusions (1 h).
6. Presentation of master thesis. (1 h)
7. Primary and secondary sources of scientific literature. Databases of scientific literature (1 h. 2h pr.w.)
8. Discussion of basic information of master thesis: examples. (4 h) (seminar)
9. Types of research. Structure of research work (1 h)
10. Planning and implementation of experiment, its results, and their interpretation: examples (seminar) (4 h).
11. Types of scientific articles. Content and tasks of separate sections of scientific article (1 h).
12. Statistical sets. The problem of representativeness Empirical and theoretical distributions (1 h).
13. Selection of appropriate data processing method (1 h).
14. Descriptive statistics: parameters, methods, implementation possibilities (2 h laboratory work).
15. Hypothesis testing. Null hypothesis (1 h laboratory work).
16. Parametric and non-parametric methods (1 h laboratory work).
17. Single factor and multiple strategy. Nonlinearity. (1 h laboratory work).
Test.
18. Analysis of variance (ANOVA). Interdependence of traits. (1 h lectures. 1 h laboratory work).
19. Correlation analysis. Regression analysis (1h lectures. 1 h - laboratory works).
20. Multiparameter classification (1 h lectures.).
Test
21. The essence and role of the study course in the research of forest ecosystem processes, data collection, processing, analysis, and interpretation of results. Scientific research and data acquisition. Classification of empirical research data. (Lecture– 1 hour)
22. Statistical indicators, their classification and significance. (Lecture– 1 hours, Practice – 1 hours)
23. Theoretical distributions, use of their regularities. Discrete and continuous random variable. Normal distribution, its role in the research data processing process. (Lecture– 1 hours)
24. The essence of parametric and non-parametric methods and conditions of use. (Lecture– 1 hour)
Test. Descriptive statistics. Theoretical distributions and the use of their regularities in research.
25. Statistical hypothesis and its task statement. Statistical conclusions and decisions. Hypothesis testing algorithm. (Lecture– 1 hours)
26. Hypothesis testing with a test evaluation approach. Hypothesis testing with p-value approach. (Lecture– 1 hours, Practice – 1 hours)
27. Verification of the correspondence between the empirical and theoretical distributions. Task statement in research. (Lecture– 1 hours, Practice – 1 hours)
28. Analysis of variance. The basic concepts of experimental design. One-way analysis of variance for differences among the means of several groups. Two-way analysis of variance and the interaction effect. Interpretation of results. (Lecture– 1 hours, Practice – 2 hours)
Test. Parametric hypothesis testing methods and their use in research.
29. Non-parametric methods for comparing data from two unrelated sets. Mann-Whitney U test. Kolmogorov-Smirnov method. Series criterion. (Lecture– 1 hours, Practice – 2 hours)
30. Non-parametric methods for comparing data from two related sets. Wilkoxon test. Character test. Mak Nemara test. (Lecture– 1 hours, Practice – 1 hours)
31. Comparison of data from several unrelated sets. Kruskala-Valisa test. Median test. (Lecture– 1 hours, Practice – 1 hours)
32. Comparison of data from several related sets. Friedman test. Kendela W test. Kohrana Q test. (Lecture– 1 hours, Practice – 1 hours)
33. Chi-square criterion for checking the adequacy of qualitative samples. Contingency analysis. (Lecture– 1 hours, Practice – 1 hours)
34. Characteristic relationship analysis for nominal scale and ordinal scale data. (Lecture– 1 hours, Practice – 1 hours)
Test. Nonparametric hypothesis testing methods and their use in research. (1 hour)
35. Correlation analysis. Purpose of correlation analysis. Linear and nonlinear correlation. Positive and negative correlation. Pair and two-factor correlation. Linear correlation coefficient. Evaluation of correlation coefficients, comparison. Correlation diagrams. Decision making and interpretation of results. (Lecture– 1 hours, Practice – 2 hours)
36. Regression analysis. Purpose of regression analysis. Alignment of empirical data. Analytical method. Linear regression. Coefficient of determination. Determination and use of regression model. Decision making and interpretation of results. (Lecture– 1 hours, Practice – 2 hours)
Test. Correlation analysis, regression analysis and their use in research.

Requirements for awarding credit points

Successfully passed the exam.
The assessment of the exam depends on the cumulative assessment of the semester:
• overall assessment of written tests.
• theory test (in writing).
Must be prepared for the structure and sections of your research (master's thesis): purpose, tasks, materials and methods. The seminar and practical work must be attended and
all tests must be successfully written. All laboratory work must be completed and defended (score counted/unaccounted for).
Execution of delayed laboratory work and seminars and rewriting of uncounted tests at the time specified by the Department of Forestry.

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 can apply the acquired theoretical knowledge and skills.

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību centrs, 2003. 352 lpp.
2. 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, 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 basic study courses of the Master's study program in Forestry.