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Data Processing and Analytics

Course developers

Course abstract

Students improve their understanding of the empirical part of the research, acquire knowledge on creating a research sample and preparing of questionnaires, develop knowledge and skills necessary for evaluating and publishing research results, and improve the ability to critically evaluate research reports. In practical classes, students gain experience in data processing, improve primary data processing and presentation, as well as secondary data processing, interpretation and presentation skills.
The aim of the study course is to provide students with in-depth knowledge and skills in evaluating research results using primary and secondary data processing, interpretation and presentation skills.

Learning outcomes and their assessment

Knowledge:
• Able to characterize the quality and internal consistency of the questionnaires – tests;
• Able to justify the selection of the research sample – tests;
• Able to justify the choice of data processing methods for obtaining inferential statistics – tests.
Professional skills:
• Able to describe and justify the process of empirical research – practical works;
• Able to describe the basis of the research– practical works;
• Able to choose methods of data acquisition, primary processing and display appropriate to the purpose and tasks of the research – practical works;
• Able to justify the design of the empirical research theoretically – practical works;
• Able to choose appropriate data processing methods for obtaining inferential statistics– practical works;
• Able to propose and test statistical hypotheses – practical works
Soft skills:
• Able to participate in research planning and goal setting - practical works;
• Able to develop professional cooperation and organizational skills - practical work, 1st-4th independent works.
Competence:
• Able to independently create an internally agreed survey page, summarize research results and reflect them in tables and diagrams using computer programs - 1st-4th independent works;
• In accordance with the methodological requirements of research, is able to independently create references and bibliography, as well as perform research and present research results - 1st-4th independent works;
• In accordance with the methodological requirements of research, able to independently develop and present a research report or scientific article - 1st-4th independent works.

Course Content(Calendar)

1. The role of empirical studies in the research (lecture - 1 h)
2. Characterization of the research base in the research report (lecture - 1 h)
3. Respondents: population and sample. Sampling (lecture - 1 h)
4. Data collection methods. Measurement scales. Data validity (lecture - 1 h)
5. Pilot studies and correction of data collection methodology (lecture - 1 h)
6. Basics of the developing of questionnaires. Making a questionnaire on Google Forms (lecture - 1 h, practical work - 2 h)
7. Evaluation of the internal consistency of the questionnaires (Cronbach's alpha) (lecture - 1 h, practical work - 1 h)
8. Data grouping techniques. Data validation (lecture – 1 h)
9. Types of experiments and plans of experiments (lecture - 1 h, practical work - 1 h)
10. Data processing (descriptive statistics). Representation of data in tables and charts (MS Excel) (lecture - 1 h, practical work - 3 h)
11. Use of computer programs (R, SPSS), online in data processing (descriptive statistics) (practical work – 3 h)
12. Method of expert opinions (R, SPSS), online: justification, competence and consensus of experts' choice (practical work - 1 h)
First test. Data collection and processing methods (descriptive statistics) – 1 h
13. Data distributions. Selection of inferential statistics (lecture - 1 h, practical work - 1 h)
14. Parametric and non-parametric statistical methods (R, SPSS), online (lecture - 1 h, practical work - 2 h)
15. Contingency analysis. Calculation of correlation coefficients (practical work - 1 h)
16. Data processing (inferential statistics). Testing of statistical hypothesis (R, SPSS), online (practical work – 4 h)
17. Research results in research reports. The importance of the discussion section in scientific publications (practical work - 1 h)
18. Self-evaluation of the scientific report, preparation of research defence speech (practical work – 2 h)
Second test. Statistical hypothesis analysis, using inferential statistics – 1 h

Requirements for awarding credit points

The course includes 2 tests, two independent works. Practical works to be done during practical work in the classroom. All work must be successfully completed.

Description of the organization and tasks of students’ independent work

Within the study course, for independent works is given 84 hours. Independent works are organized as follows: preparation for the test (12 hours for each work), preparation for independent works (30 hours for each work).

Criteria for Evaluating Learning Outcomes

The final grade in the study course is formed by:
20% practical works – making a questionnaire, representation of data and data processing, using descriptive and inferential statistics;
30% tests – data processing methods using descriptive and inferential statistics;
50% two independent works – data collection and statistical processing.

Compulsory reading

1. Pētniecība: teorija un prakse / Kristīnes Mārtinsones, Anitas Piperes, Daigas Kamerādes zinātniskajā redakcijā. Rīga: RaKa, 2016. 546 lpp.
2. Geske A., Grīnfelds A. Izglītības pētījumu aptaujas – no izveidošanas līdz datu apstrādei. Rīga: LU Akadēmiskais apgāds, 2020. 167 lpp.
3. Wilkinson, D., Dokter, D. The Researcher’s Toolkit: The Complete Guide to Practitioner Research (2nd ed). Routledge. 2023. 168 p.
4. Research Methods for Human Resource Management. K.Sanders, J.A. Cogin, & H.T.J. Bainbridge (Eds.), Routledge. 2014. 161 p.
5. Kroplijs A., Raščevska M. Kvalitatīvās pētniecības metodes sociālajās zinātnēs. Rīga: RaKa, 2004. 178 lpp.

Further reading

1. Mood D.P., Morrow J.R., McQueen M.B. Statistics in Human Performance Using SPSS and R. New York. 2020. 443 p.
2. Okello G.O. Simplified Business Statistics Using SPSS. CRC Press. 2023. 469 p.
3. Charry K., Coussement K., Demoulin N., Heuvinck N. Marketing Research with IBM SPSS Statistics. Out of House Publishing. 2016. 265 p.

Periodicals and other sources

1. Abonētās datubāzes LBTU tīklā. Pieejams: http://llufb.llu.lv/db.html?i=db_saraksti.html
2. Pārresoru koordinācijas centra pētījumu un publikāciju datu bāze. Pieejams: http://petijumi.mk.gov.lv/
3. LR Izglītības ministrijas publikācijas un statistika. Pieejams: http://www.izm.gov.lv/lv/publikacijas-un-statistika
4. Latvijas Zinātnes padome. Zinātnisko publikāciju klasifikācija. Pieejams: http://www.lzp.gov.lv
5. LZA Terminoloģijas komisija: Par pētījuma zinātniskā stipruma kritēriju terminiem. Pieejams: https://likumi.lv/doc.php?id=232703

Notes

Compulsory study course in the professional master's study programme Human Resource Management