Statistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:6.00

Study Course Accepted:27.10.2023 08:55:38

Study Course Information
Course Code:		SL_020		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:		23 Kapselu street, 2nd floor, 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 of basic statistical methods use in statistic analysis and introduce to IBM SPSS.
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 MS Excel. Introduction in IBM SPSS. Basic actions with data in the IBM SPSS.			Classes		1.00	computer room
2	Descriptive statistics in MS Excel and IBM SPSS.			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 sample comparison.			Classes		1.00	computer room
6	Non-Parametric statistics for quantitative data. Independent and dependent sample comparison.			Classes		1.00	computer room
7	Qualitative data processing. Dependent and independent samples.			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.			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 MS Excel. Introduction in IBM SPSS. Basic actions with data in the IBM SPSS.			Classes		1.00	computer room
2	Descriptive statistics in MS Excel and IBM SPSS.			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 sample comparison.			Classes		1.00	computer room
6	Non-Parametric statistics for quantitative data. Independent and dependent sample comparison.			Classes		1.00	computer room
7	Qualitative data processing. Dependent and independent samples.			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.			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 MS Excel, SPSS offered probabilities in data processing and visualising; * know the criteria for using data processing methods; * interpret the main statistical indicators.
Skills:		Upon successful acquisition of the course, the students will be able to: * set up and edit database in MS Excel and IBM SPSS; * precisely prepare data for statistical analysis; * choose correct data processing methods, that is to do statistical hypothesis; * process data in MS Excel and IBM SPSS; * create and edit tables, graphics in Excel and IBM SPSS; * 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	Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007, p 155.
2	Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition, 2013.
3	Petrie A. & Sabin C. Medical Statistics at a Glance. 3rd edition, 2009.