Veidlapa Nr. M-3 (8)
Study Course Description
Methods of Quantitative Analysis
Course Code
SL_042
Branch of Science
Economics and Business; Statistics
ECTS
3.00
Target Audience
Business Management
LQF
Level 7
Study Type And Form
Full-Time; Part-Time
Study Course Implementer
Course Supervisor
Diāna Kalniņa
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
Riga, 14 Baložu iela, Block A, E-mail: statistika@rsu.lv, Phone: +37167060897
About Study Course
Objective
To provide knowledge about commonly used quantitative methods for solving economic problems and basic skills in applying quantitative methods. To acquire basic knowledge and skills in statistical data processing methods, which are necessary for the development of scientific research work and application of statistical indicators in specialty.
Preliminary Knowledge
Prior knowledge in mathematics and computer science.
Learning Outcomes
Knowledge
1.After the completion of the course, students gain knowledge and overview of methods of quantitative analysis and data processing, to be used in analszing research data and interpreting results.
Skills
1.Students will acquire the skills to work with the statistical program SPSS, will learn to evaluate the accuracy and reliability of quantitative research results available in the public space, as well as to independently process quantitative research data, analyze statistical indicators and draw correct conclusions.
Competences
1.After the completion of the course, students are able to practically apply the acquired knowledge on the use of quantitative analysis methods and data analysis.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Students study literature and e-study materials outside classes and lectures. Independent work is done both in groups and individually, homework is prepared, preparations for seminars and exam.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Attendance – 10%, tests – 40%, exam – 50%.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Types and methods of quantitative research. Their advantages and limitations. Primary, secondary and tertiary data. Scales.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Analysis of quantitative research data in IBM SPSS program, descriptive statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Inferential statistics. Selection of an appropriate test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Concepts, operationalisation of concepts, study hypotheses. Preparation of research materials, types of questions.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Concepts, operationalisation of concepts, study hypotheses. Preparation of research materials, types of questions.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Inferential statistics. Selection of an appropriate test.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Inferential statistics. Selection of an appropriate test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
General population, target group and sample. Types of sampling and sampling principles. Data weighing. Accuracy and reliability of research results.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Introduction to the IBM SPSS Statistical Data Processing Program. Basic operations with data in the IBM SPSS program (data entry, description, quality check). Practical file creation. Coding, creation of new variables.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Introduction to the IBM SPSS Statistical Data Processing Program. Basic operations with data in the IBM SPSS program (data entry, description, quality check). Practical file creation. Coding, creation of new variables.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
20 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Inferential statistics. Selection of an appropriate test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Analysis of quantitative research data in IBM SPSS program, descriptive statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Concepts, operationalisation of concepts, study hypotheses. Preparation of research materials, types of questions.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Types and methods of quantitative research. Their advantages and limitations. Primary, secondary and tertiary data. Scales.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
General population, target group and sample. Types of sampling and sampling principles. Data weighing. Accuracy and reliability of research results.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Inferential statistics. Selection of an appropriate test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Introduction to the IBM SPSS Statistical Data Processing Program. Basic operations with data in the IBM SPSS program (data entry, description, quality check). Practical file creation. Coding, creation of new variables.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
14 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Pētniecība: teorija un prakse. (2016). K. Mārtinsones, A. Piperes, D. Kamerādes zin. red. Rīga: RAKA.
2.
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. 5th ed. SAGE
3.
Hair, J. F., et al. (2010). Multivariate Data Analysis. Upper Saddle River, NJ [u.a.]. Pearson Prentice Hall.
4.
Počs, R. (2003). Kvantitatīvās metodes ekonomikā un vadīšanā. Rīga, RTU.
Additional Reading
1.
Walters D. W., Walters D. J. (2008). Quantitative Methods for Business. Pearson Education.
2.
Curwin J., Slater, R. (2008). Quantitative Methods for Business Decisions.
3.
Swift, L., Piff, S. (2010). Quantitative Methods for Business, Management and Finance. Hampshire: Palgrave Macmillan. 812p.
4.
Blair, J., Czaja, R. F., Blair, E. (2014). Designing Surveys: A Guide to Decisions and Procedures. Thousand Oaks, Calif, SAGE.
Other Information Sources
1.
Choosing the Correct Statistical Test in SAS, STATA and SPSS. http://stats.idre.ucla.edu/other/mult-pkg/whatstat/
2.
Selecting Statistics. http://www.socialresearhmethods.net/selstat/ssstart.htm
3.
We make statistics easy. The ultimate IBM® SPSS® Statistics guides. https://statistics.laerd.com/
4.
Statistics tutorials. Available from: www.statsoft.com/textbook/stathome.html