Veidlapa Nr. M-3 (8)
Study Course Description
Analysis of Social Research Data
Course Code
LUSDK_264
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Social Welfare and Social Work
LQF
Level 7
Study Type And Form
Full-Time; Part-Time
Study Course Implementer
Course Supervisor
Ināra Kantāne
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
14 Baložu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To provide knowledge and skills in statistical data processing methods required for the development of master's thesis and the application of statistical indicators in their specialty.
Preliminary Knowledge
Basic knowledge in mathematics and informatics.
Learning Outcomes
Knowledge
1.Upon successful acquisition of the course, students' knowledge will allow them to: * recognise statistical terminology and basic methods used in scientific publications; * know IBM SPSS Statstics offered probabilities in data processing and visualising; * know the criteria for using data processing methods; * interpret the research results.
Skills
1.Upon successful acquisition of the course, the students will be able to: * set up and edit database in Excel and IBM SPSS Statistics; * precisely prepare data for statistical analysis; * choose correct data processing methods; * process data in IBM SPSS Statstics; * create and edit tables, graphics in Excel and IBM SPSS Statistics; * correctly describe the results.
Competences
1.Upon successful acquisition of the course, students will be able to decide what method to use for analysis and with the help of programs Excel and IBM SPSS analyse the data with the acquired knowledge.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
1.Individual work with literature – preparation for each class accordingly to the topics.
2. Analysis of a scientific publication.
3. Individual work – data file for each student is made (or he can use his own data), tasks are predefined: decriptive statistics, inferential statistics, reporting of the results and presenation of them.
In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Participation in practical lectures. For every missed lecture – summary has to be written using given literature (min. 1 A4 page).
After completion of this course:
1. Oral presentation of independent work – 50%.
2. Exam - multiple choice test with theoretical questions in statistics – 50%.
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||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction in statistics, the role of statistics in research process.
Data types, measure, data input, data preparation in Excel.
Introduction in IBM SPSS Statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in Excel and IBM SPSS Statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Normal distribution and its descriptive statistics indicators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical hypothesis, types of statistical hypothesis.
Hypothesis testing. P value.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Parametric statistics for quantitative data. Independent samples and dependent samples comparison.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Non-Parametric statistics methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Qualitative data processing methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Correlation analysis. Regression analysis (Linear regression).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Regression analysis (Binary logistics regression).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Scientific research analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Individual work with data in IBM SPSS Statstics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Students presentations.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction in statistics, the role of statistics in research process.
Data types, measure, data input, data preparation in Excel.
Introduction in IBM SPSS Statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in Excel and IBM SPSS Statistics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Normal distribution and its descriptive statistics indicators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical hypothesis, types of statistical hypothesis.
Hypothesis testing. P value.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Parametric statistics for quantitative data. Independent samples and dependent samples comparison.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Non-Parametric statistics methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Qualitative data processing methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Correlation analysis. Regression analysis (Linear regression).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Regression analysis (Binary logistics regression).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Scientific research analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Individual work with data in IBM SPSS Statstics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Students presentations.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Peat, J. & Barton, B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition. John Wiley & Sons, 2014.
2.
Field, A. Discovering Statistics using IBM SPSS Statistics. 2018.
Additional Reading
1.
Teibe, U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds. 2007, p 155.