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Research Methodology

Course developer

Course abstract

The aim and tasks of the course:
• General training of students in statistics to
o give an idea of the use of statistical methods in science;
o enable creative use of statistics in the specialty;
o facilitate studies in other science-related areas of study that use statistics;
• Provide basic knowledge for obtaining, processing, analyzing and interpreting data methodologically correctly;

• Provide basic skills for solving various types of statistical tasks using MS Excel, SPSS and R.

Learning outcomes and their assessment

Academic competences:
• Basic knowledge of statistical theory and application;
• Knowledge of the principles of planning, implementation and approbation of research work.
Professional competences:
• Acquired basic concepts of statistics and probability theory;
• Can interpret and solve standard tasks using Excel and R;
• Improved independent work skills for discovering other statistical methods.

The control of the level of skills and competence is based on the results demonstrated in the tests and practical works and the assessment is reflected in the evaluation of the tests, practical works and examination.

Course Content(Calendar)

Main topics of the course:
• Data mining and presentation (2 lect. 3 pr.)
• Descriptive statistics (2 lect. 3 pr.)
• Basics of probability theory (2 lect.)
• Random variable distributions (4 lect.)
• Research design, sampling method (2 lect. 2 pr.)
• Confidence interval (2 lect. 2 pr.)
• Hypothesis testing - parametric methods (4 lect. 6 pr.)
• Hypothesis testing - nonparametric methods (4 lect. 4 pr.)

• Correlation and linear regression (2 lect. 4 pr.)

Requirements for awarding credit points

Requirements:
• solved and accepted tests and homeworks;

• during the exam the understanding of the lecture material is tested (verbally).

Description of the organization and tasks of students’ independent work

Tests: seminars and tasks during the lectures

Homeworks: presentations on individual research and selected course topics. Individual assignment of data analysis.

Criteria for Evaluating Learning Outcomes

In the final assessment, the proportion of tests, homeworks and exam is:
• 60% (tests and homeworks);

• 40% (exam).

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību centrs, 2003. 349 lpp.
2. Lapiņš D. Pētījumu metodoloģija. Lekciju konspekts. Jelgava: LLU, LF, 2010. 80 lpp.

3. Paura L., Arhipova I. Neparametriskās metodes. SPSS datorprogrammas. Jelgava: LLKC, 2002. 148 lpp.

Further reading

1. Mark L. Berenson, David M. Levine, Kathryn A. Szabat. Basic Business Statistics. Pearson, 2015, 859 p.
2. Field., A., Doscovering Statistics Using SPSS. SAGE Publications, 2005, 779 p.
3. Sahu, P.K. Applied Statistics for Agriculture, Veterinary, Fishery, Dairy and allied Fields. Springer, 2016., 533 p.
4. Hardy M., Bryman A., Handbook of data analysis. SAGE Publications, 2004. 704 p.
5. Goša Z. Statistika. Rīga: LLU, 2003. 334 lpp.
6. Krastiņš O., Ciemiņa I. Statistika. – Rīga, Latvijas Republikas Centrālā statistikas pārvalde, 2003., 267 lpp.

7. Krastiņš O. Varbūtību teorija un matemātiskā statistika. R.: Zvaigzne,1978.

Periodicals and other sources

1. Latvijas Lauksaimniecības universitātes Raksti. Latvijas Lauksaimniecības universitāte. ISSN 1407-4427.

2. Agronomijas Vēstis: zinātnisko rakstu krājums. Latvijas Lauksaimniecības un meža zinātņu akadēmija, Latvijas Lauksaimniecības Universitāte, Lauksaimniecības fakultāte. ISBN 9984555895(5). ISSN 1691-3485.

Notes

Master's study programme.