Course code Mate2012

Credit points 3

Mathematical Methods in the Social Research

Total Hours in Course81

Number of hours for lectures16

Number of hours for seminars and practical classes16

Independent study hours49

Date of course confirmation18.10.2021

Responsible UnitInstitute of Computer Systems and Data Science

Course developer

author Datoru sistēmu un datu zinātnes institūts

Laima Bērziņa


Course abstract

Students acquire understanding of statistical methods importance to make accurate judgments and logical conclusions in the scientific work processes. Students acquire skills to use mathematical statistics methods for statistical hypothesis testing to compare means, as well as testing hypothesis with correlation and regression analysis.

Learning outcomes and their assessment

Knowledge – to understand the importance of mathematical statistics in sociology, covering traditional methods but with greater emphasis on its practical application for surveys of social processes (laboratory works). Skills – to use appropriate mathematical statistical methods of data analysis, interpret numerical information and results of statistical analyses, use computer statistical packages and interpret the outputs (laboratory works, tests). Competence – to analyse and present data analysis results of social research by using statistical analysis outcome (independent work).

Course Content(Calendar)

1. Application of mathematical methods in social sciences.
2. Basics of probability theory.
3. Probability distributions. Normal distribution law.
4. Population and sample data. Evaluation of population parameters.
5. Statistical hypotheses. Classification of statistical tests.
6. Hypothesis for comparing data distributions.
7. 1st test.
8. t-test for dependent samples.
9. Non-parametric statistical methods for dependent samples.
10. t-test for independent samples.
11. Non-parametric statistical methods for independent samples.
12. Hypotheses about comparing relative frequencies.
13. Analysis of variance.
14. Testing hypotheses in correlation and regression analysis.
15. Chi-squared test.
16. 2nd test.

Requirements for awarding credit points

2 tests during the course;
representation of independent work results.

Description of the organization and tasks of students’ independent work

During the semester student has to perform independent work, which provides analysis of social survey data. The work must be written and submitted electronically on the e-learning site. Presentation of the work should be given to the audience.

Criteria for Evaluating Learning Outcomes

Cumulative course grade will be determined during the semester by the relative weights given:
independent work – 20%;
test Nr. 1 – 40%;
test Nr. 2 – 40%.

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā. Rīga: Datorzinību Centrs, 2006. 364 lpp.
2. Raizs Ļ. Matemātiskās metodes sociālajās zinātnēs. Rīga: RaKa, 2000. 296 lpp.
3. Grīnglazs L., Kopitovs J. Matemātiskā statistika: ar datoru lietojuma paraugiem uzdevumu risināšanai. Rīga: Rīgas Starptautiskās ekonomikas un biznesa administrācijas augstskola, 2003. 310 lpp. 4. Krastiņš O. Ciemiņa I. Statistika. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp
4. Diez D. M. Advanced High School Statistics. OpenIntro 2017. 458 p. Available:
5. Watkins J.C. An Introduction to the Science of Statistics: From Theory to Implementation Available:
6. Coates G. Inferential Statistics (testing hypotheses). Available:

Further reading

1. Krastiņš O. Statistika un ekonometrija. Rīga: LR Centrālā statistikas pārvalde, 1998. 436 lpp.
2. Raščevska M. Kristapsone S. Statistika psiholoģijas pētījumos. Rīga: Izglītības soļi, 2000. 356 lpp.
3. Gill J. Essential Mathematics for Political and Social Research (Analytical Methods for Social Research). Cambridge University Press 2006. 448 p
4. Mathematics higher level: statistics : course companion / Josip Harcet ... [et al.]. Oxford : Oxford University Press, 2014. 162 p.
5. Statistics for international social work and other behavioral sciences / Serge C. Lee, Maria C. Silveira Nunes Dinis, Lois Lowe, Kelly Anders. Oxford ; New York : Oxford University Press, 2016. 214 p.
6. Statistics with R : solving problems using real-world data / Jenine K. Harris, Washington University in St. Louis : SAGE, 2021. 733 p.
7. Statistics without maths for psychology / Christine P. Dancey, John Reidy. Harlow, England ;New York : Pearson, 2020.608 p.
8. Statistics for business & economics / David R. Anderson, University of Cincinnati, Dennis J. Sweeney, University of Cincinnati, Thomas A. Williams, Rochester Institute of Technology, Jeffrey D. Camm, Wake Forest University, James J. Cochran, University of Alabama. Boston, MA : Cengage Learning, 2018. 1092 p.
9. Statistics for international social work and other behavioral sciences / Serge C. Lee, Maria C. Silveira Nunes Dinis, Lois Lowe, Kelly Anders. Oxford; New York : Oxford University Press, 2016. 214p.

Periodicals and other sources

Centrālās statistikas pārvaldes mājas lapa [tiešsaiste]. Pieejams:
Social Science Statistics [online]. Available:
Official Statistics Portal [online] Available:


Academic study programme Sociology of Organizations and Public Administration.