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Research Methodology

Course developer

Course abstract

Types of scientific cognition. Basic information and structure of the master's thesis. Analysis of common errors. Information gathering, mathematical processing, analysis and interpretation of results, principles and methods. The problem of representativeness. Statistical sets, their distributions, indicators and parameters. Hypothesis formulation and testing. Multivariate statistics. Specifics of processing methods and compliance with the research task and material, selection of the most suitable method. Partial master's thesis elaboration.

Learning outcomes and their assessment

In the study course master students acquire knowledge about the common regularities of scientific research implementation: formulation of basic research information (title, goal, hypothesis, research tasks), master thesis content structuring, information gathering, mathematical processing, interpretation of results and master thesis writing and defense requirements - written exam. Master students maintain contact with scientific supervisors and improve the ability to independently choose and implement research conditions for the performance of scientific work (selection and application of the most appropriate method of mathematical data processing, explanation and presentation of results) - practical work. After ending the study course, master students are competent to independently carry out scientific work and defend it - seminar: analysis of individual tasks (master's thesis essay) performance.

Course Content(Calendar)

1. Study course acquisition conditions. Basic information (title, aim, tasks) and structuring of the master's thesis. 2h lectures.
2. Analysis of examples and common mistakes. Choice of data processing method. 2h lectures.
3. Statistical sets. The problem of representativeness. 2h lectures.
4. Empirical and theoretical distributions. 2h lectures.
5. Descriptive statistics: parameters, methods, implementation possibilities. 2h lectures.
6. Hypothesis testing. Null hypothesis. 2h lectures.
7. Parametric and non-parametric methods. 2h lectures.
8. Addiction infrastructure. Single factor and multiple strategy. Nonlinearity. Synergies. 2h lectures. 2h lectures.
9. Analysis of variance. Feature dependence. 2h lectures. 1h practical work.
10. Dependence of features. Correlation analysis. 2h lectures.
11. Dependence of features. Regression analysis. 2h lectures. 1h practical work.
12. Multiparameter classification. 2h lectures.
13. Seminar: analysis of individual tasks (master's thesis essay) performance. 2h practical works.
14. Seminar: analysis of individual tasks (master's thesis essay) performance. 2h practical works.
15. Seminar: analysis of individual tasks (master's thesis essay) performance. 2h practical works.
16. Examination.

Requirements for awarding credit points

Must have accepted an individual task for the seminar, submitted and credited a master's thesis essay. Successfully passed written exam.
Attendance - at least 50% of lectures and practical work.

Description of the organization and tasks of students’ independent work

Individual task (master's thesis essay). The essay contains the basic information of the research (topic, aim, hypothesis, research tasks), the content of the master's thesis, a brief overview of the literature, a description of the work methodology and possible, predicted conclusions. Minimum essay volume is at least 20 pages. The essay of the master's thesis must be submitted in an electronic (MS Word document) and printed form, as well as presented in the seminar in the form of a PowerPoint presentation.

Criteria for Evaluating Learning Outcomes

The final assessment of the study course depends on the assessment obtained in the exam.

A Master's student can obtain a successful mark on the exam if at least 50% of the content of the exam questions is answered correctly and completely.
The independent work (master's thesis essay) is evaluated in accordance with the evaluation procedure specified in the independent work task.
The student is admitted to the exam after successful defense of the individual task (master's thesis essay).

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību centrs, 2003. 352 lpp.
2. Williams B. Biostatistics. Concepts and Applications for Biologist. London: Chapman & Hall, 1996. 201 p. 3. Zar J. H. Biostatistical Analysis. New Jersey: Published by Prentice Hall, 1999. 591 p.

Further reading

1.Sprent P., Smeeton N.C. Applied Nonparametric Statistical Methods. Boca Raton, London, New York, Washington D.C.: Chapman Hall/CRC, 2000. 461 p.

Periodicals and other sources

1.Latvijas Lauksaimniecības Universitāte. Raksti. ISSN: 1407-4427. Rural Sustainability Research. Proceedings of the Latvia University of Agriculture. Berlin: De Gruyter Open. ISSN: 2256-0939.
2. Zinātnisko konferenču rakstu krājumi izvēlētajā zinātnes apakšnozarē.
3. Mežzinātne. ISSN: 1407-270X.
4. Biometrics. A Journal of the International Biometric Society. ISSN: 0006-341X.

Notes

Study course compulsory for students of specialization direction “Forest ecology and silviculture” of master study programme “Forest science”.