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Research Methodology and Data Analysis

Course developer

Course abstract

Planning of research and choice of method is discussed underlying significance of information (literature) review. Choice of method for data acquisition, and mastering the skills for data analysis with software means. Empirical distributions, determination and statistical tests of characteristics. Theoretical distributions. Analysis of correlation. Analysis of one and multiple factor regression. Tests of confidence level of statistics obtained result in. General analysis of research process and results, making of conclusions and/or decision.

Learning outcomes and their assessment

• Knowledge: for setting the target and tasks of research, for choice of the appropriate research method. Knowledge on analysis of empirical data using theoretical models for variation analysis; on examination of results
• Skills: to discuss about principles of choice the methods and their application and implementation regarding specific research problem; skills for use of appropriate software for data analysis.
• Competence: ability for comprehensive thinking; to interpret the results, draw conclusions, make decisions and assess its significance.

Course Content(Calendar)

1 Objectives and motivation in research. Distinct approaches and significance of research. Finding a topic. State-Of-Art review. Formulating the research problem. 3 h
2 Knowledge, science and truth. Science and ethics. Measure of good research. Common problems for researchers. 3 h
3 Research methodology versus research methods. Overview of research methods. 3 h
4 Statistical methods for data analysis. Statistics of one dimensional sample. Checking normality. 3 h
5 Normal and lognormal distributions. Probability density functions (PDF) 3 h
6 Other theoretical distributions, criteria for applicability 3 h
7 General theory for testing and confidence intervals 3 h
8 Two sample problem. Testing of the variances. Correlation 2 h
9 Simple linear and polynomial regression. Estimation of parameters.
11 Writing a good thesis: Research report writing 3 h

Requirements for awarding credit points

Credit test with mark will be enrolled after successful discussion with instructor on course topics, as well as competence demonstrated in discussion on results of home work.

Description of the organization and tasks of students’ independent work

Home work: “Analysis of research data”. Instructor provides master student with an individual task including data samples to be analysed, specifying the deadline of submitting the report. It is good option for master student to take tutorials and discussion with instructor.

Criteria for Evaluating Learning Outcomes

The positive assessment of test will be given if at least 50 percents of test questions are answered correctly.
Homework is assessed basing on two criteria: 1) is it appropriate choice of statistical methods for data analysis, and results obtained are confident, and critical conclusions have been delivered
2) ability to discuss on methods and results of data analysis.

Compulsory reading

1. Ievads pētniecībā :stratēģijas, dizaini, metodes /[sastādītāja Kristīne Mārtinsone]. [Rīga] : RaKa, 2011. 284 lpp.
2. Smotrovs, Jānis.: Varbūtību teorija un matemātiskā statistika: [mācību grāmata dabaszinātņu un inženierzinātņu studentiem]. Rīga : Zvaigzne ABC, 2004-2007. 2 sēj.
3. Arhipova, Irina: Varbūtību teorijas un matemātiskās statistikas pielietojumi inženierzinātnēs: mācību līdzeklis/ Irina Arhipova ; Latvijas Lauksaimniecības universitāte. Informācijas tehnoloģiju fakultāte. Jelgava: LLU, 2008. 125 lpp.
4. Kottegoda, Nathabandu T.: Applied statistics for civil and environmental engineers /Nathabandu T. Kottegoda, Renzo Rosso. Oxford ; Malden, MA : Blackwell Publishing, 2008. 718 pp.

Further reading

1. Hofmann, Angelika H. Scientific writing and communication : papers, proposals, and presentations /Angelika H. Hofmann. New York ; Oxford : Oxford University Press, 2010., 682 pp.
2. Paura, Līga. Neparametriskas metodes : SPSS datorprogramma : [māc. līdz.] /Līga Paura, Irina Arhipova. Jelgava : LLKC, 2002. 148 lpp.
3. Day, Robert A. How to write and publish a scientific paper /Robert A. Day, Barbara Gastel. Cambridge: Cambridge University Press, 2006., 302 lpp.
4. Statistics for Earth and Environmental Scientists/ John H. Schuenemeyer, Lawrence J. Drew. Hoboken, New Jersey: John Wiley & Sons, 2011, 407 lpp. LLU:Uzziņu un inform. centrs

Periodicals and other sources

1. GEO : izzināt un saprast pasauli. Rīga: Izdevniecība Lilita Z. ISSN 1691-5046

Notes

Compulsory study course for the Academic Master's study programme “Environmental, Water and Land Engineering” and the Professional Master's study programme “Civil Engineering”