Analysis of Social Research Data

Study Course Description

Course Description Statuss:Approved

Course Description Version:5.00

Study Course Accepted:26.08.2024 13:10:59

Study Course Information
Course Code:		LUSDK_264		LQF level:		Level 7
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Social Welfare and Social Work
Study Course Supervisor
Course Supervisor:		Ināra Kantāne
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		14 Baložu street, Block A, Riga, +371 67060897, statistikarsu[pnkts]lv, www.rsu.lv/statlab
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		0	Lecture Length (academic hours)		0	Total Contact Hours of Lectures		0
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								24
Part-Time - Semester No.1
Lectures (count)		0	Lecture Length (academic hours)		0	Total Contact Hours of Lectures		0
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								24
Study course description
Preliminary Knowledge:
Basic knowledge in mathematics and informatics.
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.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction in statistics, the role of statistics in research process. Data types, measure, data input, data preparation in Excel. Introduction in IBM SPSS Statistics.			Classes		1.00	computer room
2	Descriptive statistics in Excel and IBM SPSS Statistics.			Classes		1.00	computer room
3	Normal distribution and its descriptive statistics indicators.			Classes		1.00	computer room
4	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value.			Classes		1.00	computer room
5	Parametric statistics for quantitative data. Independent samples and dependent samples comparison.			Classes		1.00	computer room
6	Non-Parametric statistics methods.			Classes		1.00	computer room
7	Qualitative data processing methods.			Classes		1.00	computer room
8	Correlation analysis. Regression analysis (Linear regression).			Classes		1.00	computer room
9	Regression analysis (Binary logistics regression).			Classes		1.00	computer room
10	Scientific research analysis.			Classes		1.00	computer room
11	Individual work with data in IBM SPSS Statstics.			Classes		1.00	computer room
12	Students presentations.			Classes		1.00	computer room
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction in statistics, the role of statistics in research process. Data types, measure, data input, data preparation in Excel. Introduction in IBM SPSS Statistics.			Classes		1.00	computer room
2	Descriptive statistics in Excel and IBM SPSS Statistics.			Classes		1.00	computer room
3	Normal distribution and its descriptive statistics indicators.			Classes		1.00	computer room
4	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value.			Classes		1.00	computer room
5	Parametric statistics for quantitative data. Independent samples and dependent samples comparison.			Classes		1.00	computer room
6	Non-Parametric statistics methods.			Classes		1.00	computer room
7	Qualitative data processing methods.			Classes		1.00	computer room
8	Correlation analysis. Regression analysis (Linear regression).			Classes		1.00	computer room
9	Regression analysis (Binary logistics regression).			Classes		1.00	computer room
10	Scientific research analysis.			Classes		1.00	computer room
11	Individual work with data in IBM SPSS Statstics.			Classes		1.00	computer room
12	Students presentations.			Classes		1.00	computer room
Assessment
Unaided 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.
Assessment Criteria:
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%.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		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:		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.
Competencies:		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.
Bibliography
No.	Reference
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.