Course code EkonD124

Credit points 4

Research Methodology in Economics II

Total Hours in Course160

Number of hours for seminars and practical classes32

Independent study hours128

Date of course confirmation26.02.2019

Responsible UnitInstitute of Economics and Regional Development

Course developers

author prof.

Pēteris Rivža

Dr. habil. sc. ing.

author Sociālo un humanitāro zinātņu institūts

Signe Dobelniece

Dr. phil.

author Vadības sistēmu katedra

Līga Zvirgzdiņa

Dr. oec.

author prof.

Andra Zvirbule

Dr. oec.

Prior knowledge

EkonD123, Research Methodology in Economics I

Course abstract

Study course will focus on the highest level of practical knowledge, highly developed understanding and specialized skills in the application of methods in economic research, which provides the basis for new ideas and innovative research. The course program is designed so that PhD students, taking into account the multi-criteria nature of information and making choices from several alternatives, acquire in-depth knowledge of methods of data ranking and hierarchy analysis, computer modeling of economic processes, methods of surveys and interviews data processing and interpretation, critically understand the principles of preparation and processing of experimental data with SPSS computer program, is able to demonstrate the specialized skills and work techniques in application of quantitative methods in economic research.

Learning outcomes and their assessment

Knowledge - the highest level knowledge of experimental data preparation and processing principles to work with the MS Excel and SPSS computer programs; the highest level knowledge and a critical understanding of ranking and hierarchy analysis methods and how to practically apply the methods; a profound and specific knowledge of the diversity of methods of survey and interview data processing employed in economic research, problems of interpretation of the results and solutions to the problems; the highest level knowledge and a critical understanding of the classification of economic process models and how to apply the models as well as of the computer modelling environment and relevant software.
Knowledge is assessed by the following methods: discussions, a report, practical assignments, problem case studies.
Skills - highly developed and specific skills to process data by use of MS Excel and the SPSS program, apply proper qualitative and quantitative data analysis methods needed for solving critical problems in research; critically substantiate the choice of and apply the hierarchy analysis method to make decisions in their research; select and apply in their research a proper economic process model and the relevant environment.
Skills are assessed using the following methods: report; practical assignments, discussions.
Competences - demonstrate scientific and professional independence, applying and adapting proper economic research methods to their research and thereby contributing to solving critical research problems; demonstrate an innovative approach to interpreting and structuring the research results, which creates a basis for innovative research and extension of the current knowledge.
Competences are assessed by the following methods: discussions, individual assignments, practical assignments, a report.

Course Content(Calendar)

1. Principles for preparation and processing of experimental data by means of software; practical application of quantitative methods in economic studies
Verification of hypotheses in economic studies. x2 criterion. Parametric two-sample analysis methods. Parametric multi-sample analysis methods. Correlation analysis. Regression analysis. Nonparametric two-sample analysis methods. Nonparametric multi-sample analysis methods. Principles for preparation and processing of experimental data by means of software; practical application of quantitative methods in economic studies. Time series analysis. Cluster analysis.
2. Surveys and interviews: processing and interpretation of the results
Possibilities for the analysis of survey results. Interpretation of the research results. Problems for the analysis and interpretation of quantitative research results. Options for data processing methods and combinations. Unanswered questions and skipping of the answers: interpretation and processing possibilities. Typical errors. Research data analysis and interpretation with the application of mixed methods. Processing and analysis of other quantitative social research methods.
3. Ranking method and Analytic Hierarchy Process (AHP)
Aggregate ranking methods - weighted aggregation method, target performance matrix, pair comparison analysis, transformation method. Dispersed ranking methods – for the evaluation of alternatives. Methods of the Analytical Hierarchy Process in decision-making.
In the framework of this theme, the PhD student performs individual practical task for the application of the AHP method to make decisions for two-level alternatives.
4. Introduction into computer simulation of economic processes
Classification of economic process models. Computer simulation environments and software. Criteria for selection of types of economic process models. Optimisation models. Dynamic models and their characteristics. Creation, verification, validation and application of dynamic models. Simulation models and their characteristics. Creation, verification, validation and application of simulation models. MATLAB and Simulink models and their characteristics. Application of Simulink models in economics. Decision-making models, their classification and spheres for application.

Requirements for awarding credit points

Test with mark.

Description of the organization and tasks of students’ independent work

PhD student independently performs practical work on hierarchy analysis method, two-level decision making alternatives, as well as prepares a report on data analysis and grouping by describing and analyzing the obtained results.

Criteria for Evaluating Learning Outcomes

The assessment of the study course consists of a test with an accumulative mark. Mark consists from two parts - practical work on the method of hierarchy analysis (50% of the final mark) and a report on data analysis and grouping (50% of the final mark).

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību Centrs, 2006, 362 lpp.
2. Kroplijs A., Raščevska M. Kvalitatīvās pētniecības metodes sociālajās zinātnēs. Rīga, 2004, 178 lpp.
3. Saaty Th.L. (2010) Principia Mathematica Decernendi: Mathematical Principles of Decision Making. RWS Publications, Pittsburgh, 531 p.

Further reading

1. Singh K. Quantitative social research methods. Los Angeles : SAGE Publications, 2007, 431p.
2. Bandeviča L. Matemātiskā modelēšana ekonomikā un menedžmentā (teorija un prakse): mācību grāmata augstskolām, 3.izd. (pārstrādāts un papildināts). Rīga: SIA Izglītības soļi, 2009, 443 lpp.
3. Cuaresma J.C., Palokangas T., Tarasyev A. Dynamic Systems, Economic Growth and the Environment: Dynamic Modeling and Econometrics in Economics and Finance. Springer, 2009, 289 p.
4. Jansons V., Kozlovskis K. Ekonomiskā prognozēšana SPSS 20 vidē. Rīga, RTU Izdevniecība, 2012., 547 lpp.

Notes

Science sub-field special course for PhD programme “Agrarian and regional economy”