Basics of Biostatistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:5.00

Study Course Accepted:12.12.2022 16:41:11

Study Course Information
Course Code:		SL_027		LQF level:		Level 6
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Rehabilitation
Study Course Supervisor
Course Supervisor:		Māra Grēve
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)		4	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		8
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								32
Part-Time - Semester No.1
Lectures (count)		2	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		4
Classes (count)		6	Class Length (academic hours)		2	Total Contact Hours of Classes		12
Total Contact Hours								16
Study course description
Preliminary Knowledge:
Secondary school background in mathematics and informatics.
Objective:
To get basic skills in data processing methods. To emphasize understanding in choosing correct statistical methods and visualization. To emphasize statistics role in medical research and writing for publications.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel.			Lectures		2.00	computer room
2	Basic actions in Excel and IBM SPSS			Classes		1.00	computer room
3	Descriptive statistics in Excel and SPSS			Classes		2.00	computer room
4	Inferential statistics: basic concepts, 2 group comparison (quantitative variable)			Lectures		1.00	computer room
				Classes		1.00	computer room
5	Inferential statistics: comparison of more than 2 groups (quantitative variable)			Lectures		1.00	computer room
				Classes		1.00	computer room
6	Inferential statistics: 2 and more group comparison (categorical outcome)			Classes		1.00	computer room
7	Inferential statistics: correlations, comparison of multiple quantitative and ordinal variable			Classes		1.00	computer room
8	Inferential statistics: regression models			Classes		1.00	computer room
9	Data visualization			Classes		2.00	computer room
10	Practical about statistical method representation in bachelor work			Classes		1.00	computer room
11	Individual work. Exam			Classes		1.00	computer room
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel.			Lectures		1.00	computer room
2	Basic actions in Excel and IBM SPSS			Classes		0.50	computer room
3	Descriptive statistics in Excel and SPSS			Classes		1.00	computer room
4	Inferential statistics: basic concepts, 2 group comparison (quantitative variable)			Lectures		0.50	computer room
				Classes		0.50	computer room
5	Inferential statistics: comparison of more than 2 groups (quantitative variable)			Lectures		0.50	computer room
				Classes		0.50	computer room
6	Inferential statistics: 2 and more group comparison (categorical outcome)			Classes		0.50	computer room
7	Inferential statistics: correlations, comparison of multiple quantitative and ordinal variable			Classes		0.50	computer room
8	Inferential statistics: regression models			Classes		0.50	computer room
9	Data visualization			Classes		1.00	computer room
10	Practical about statistical method representation in bachelor work			Classes		0.50	computer room
11	Individual work. Exam			Classes		0.50	computer room
Assessment
Unaided Work:
During the course students do individual work – perform calculations accordingly to given random tasks. Students can use their own data. Besides lectures, students have to study recommended literature and e-study materials, describe and interpret individuals work results in MS Word, Excel or IBM SPSS have to be used for calculations.
Assessment Criteria:
Students participation is evaluaded, individual work should be done either in Excel or IBM SPSS, results should be described in Word. The final score consists of individual work (50%), written exam (50%). For every missed lecture – a summary on the topic should be made (at least one paper, size A4).
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		Upon successful acquisition of the course, the students will be able to: 1. Work in Excel and IBM SPSS to analyse data; 2. Formulate basic principles of data processing; 3. Interpret the main statistical terms in health sport speciality.
Skills:		Upon successful acquisition of the course, the students will be able to: 1. Use MS Excel for their scientific work; 2. Make and use databases in Excel and IBM SPSS; 3. Conduct surveys for every topic in health sport speciality; 4. Collect data and correctly input them for data analysis; 5. Process data and analyse statistical indicators; 6. Make graphics in MS Excel and IBM SPSS.
Competencies:		Upon successful acquisition of the course, the students will be able to use correct statistics method in data analysis.
Bibliography
No.	Reference
Required Reading
1	Knapp H. Introductory statistics using SPSS. 2013
2	Teibe U. Bioloģiskā statistika. Rīga: Latvijas Universitāte, 2007, 156 lpp.
Additional Reading
1	Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
2	Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition, 2020.