Veidlapa Nr. M-3 (8)
Study Course Description
Research Data Analysis
Course Code
SL_030
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Psychology; Rehabilitation
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Diāna Kalniņa
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
Baložu iela 14, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To enhance MA students' comprehension of the quantitative and qualitative data processing methods; to improve the data processing skill; to develop the ability of independent decision-making about the use of suitable data processing methods in the respective situation for proving the set hypotheses or the clarification of a study issue.
Preliminary Knowledge
Bachelor's experience in research and knowledge in research methodology.
Learning Outcomes
Knowledge
1.Students use the terminology that is suitable for the research strategy (mathematical statistics terminology/qualitative research terminology); explain differences between different data processing methods; mention and characterise the data processing methods that apply to different research designs.
Skills
1.Process research data; analyse the statistical indicators; in compliance with the set hypothesis/research question, correctly describe the obtained results.
Competences
1.Apply the data processing methods that are appropriate for the particular research design; analyse and interpret the results of data processing; formulate correct conclusions concerning the approval or rejection of the hypotheses, put forward in the study, or about research questions.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
To read the indicated sources of literature independently. To perform the assigned tasks on data processing independently.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Independent processing of own data, description and presentation of the obtained results in a group.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction to data analysis. Differences in qualitative and quantitative designs. Data analysis in quantitative and qualitative designs.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction to data analysis. Differences in qualitative and quantitative designs. Data analysis in quantitative and qualitative designs.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data input and preparation for analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data input and preparation for analysis.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in qualitative design: qualitative content analysis, thematic analysis, integrative phenomenological analysis (IPA).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in qualitative design: qualitative content analysis, thematic analysis, integrative phenomenological analysis (IPA).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in qualitative design: qualitative content analysis, thematic analysis, integrative phenomenological analysis (IPA).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in qualitative design: qualitative content analysis, thematic analysis, integrative phenomenological analysis (IPA).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in quantitative design: descriptive and inferential statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis in quantitative design: descriptive and inferential statistics.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Correct description and visualisation of results in quantitative and qualitative design.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Correct description and visualisation of results in quantitative and qualitative design.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Mārtinsone, K., Pipere, A. un Kamerāde, D. (red.). (2016). Pētniecība: teorija un prakse. Rīga: RaKa.
2.
Kroplijs, A. un Raščevska, M. (2010). Kvalitatīvās pētniecības metodes sociālajās zinātnēs. Rīga: RaKa. (akceptējams izdevums)
3.
Raščevska, M. un Kristapsone, S. (2000). Statistika psiholoģijas pētījumos. Rīga: Izglītības soļi. (akceptējams izdevums)
4.
Mārtinsone, K., Perepjolkina, V. un Šneidere, K. (red.) (2020). Metodiskie norādījumi maģistra darbu izstrādei RSU veselības psiholoģijas un supervīzijas studiju programmām. Otrais, atjaunotais izdevums.
Additional Reading
1.
Leavy, P. (ed.) (2014). The Oxford Handbook of Qualitative Research. New York: Oxford University Press.
2.
SPSS for social scientists /Acton C., et.al./ Basingstoke: Palgrave Macmillan (2009). 363 lpp.
Other Information Sources
1.
Choosing the Correct Statistical Test in SAS, STATA and SPSS.