Basics of Biostatistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:11.00

Study Course Accepted:09.08.2023 11:09:49

Study Course Information
Course Code:		SL_013		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:		Ināra Kantāne
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		Baložu Street 14, 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)		11	Class Length (academic hours)		3	Total Contact Hours of Classes		33
Total Contact Hours								33
Part-Time - Semester No.1
Lectures (count)		0	Lecture Length (academic hours)		0	Total Contact Hours of Lectures		0
Classes (count)		9	Class Length (academic hours)		3	Total Contact Hours of Classes		27
Total Contact Hours								27
Study course description
Preliminary Knowledge:
Secondary school knowledge in Mathematics and Informatics.
Objective:
To get basic knowledge in data processing methods (descriptive statistics, inferential statistics to estimate differences), that can be used in bachelor's paper, analysis of scientific literature and research work in their specialty.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to statistics, the role of statistics in research process. Data preparation in Excel.			Classes		0.50	computer room
2	Introduction to IBM SPSS Statistics. Basic actions with data in the IBM SPSS Statistics program.			Classes		0.50	computer room
3	Descriptive statistics in MS Excel and IBM SPSS.			Classes		1.00	computer room
4	Descriptive statistics of the Normal distribution.			Classes		1.00	computer room
5	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Dependent and independent samples. Parametric and nonparametric data processing methods.			Classes		1.00	computer room
6	Parametric statistics for quantitative data. Comparison of independent samples and dependent samples (t test, Analysis of Variance).			Classes		1.00	computer room
7	Nonparametric statistics for quantitative data. Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis test). Comparison of dependent samples (Wilcoxon test, Friedman test).			Classes		1.00	computer room
8	Qualitative data processing. Pearson chi square test, Fisher's exact test, McNemar's test.			Classes		1.00	computer room
9	Correlation analysis. Reliability analysis. Internal consistency measure (Cronbach's alpha).			Classes		1.00	computer room
10	Summary, practical work with data. Analysis of scientific publication.			Classes		1.00	computer room
11	Independent work with data.			Classes		1.00	computer room
12	Student presentations.			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 preparation in Excel.			Classes		1.00	computer room
2	Introduction to IBM SPSS Statistics. Basic actions with data in the IBM SPSS Statistics program.			Classes		0.50	computer room
3	Descriptive statistics in MS Excel and IBM SPSS.			Classes		0.50	computer room
4	Descriptive statistics of the Normal distribution.			Classes		1.00	computer room
5	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Dependent and independent samples. Parametric and nonparametric data processing methods.			Classes		1.00	computer room
6	Parametric statistics for quantitative data. Comparison of independent samples and dependent samples (t test, Analysis of Variance).			Classes		1.00	computer room
7	Nonparametric statistics for quantitative data. Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis test). Comparison of dependent samples (Wilcoxon test, Friedman test).			Classes		1.00	computer room
8	Qualitative data processing. Pearson chi square test, Fisher's exact test, McNemar's test.			Classes		1.00	computer room
9	Correlation analysis. Reliability analysis. Internal consistency measure (Cronbach's alpha).			Classes		0.50	computer room
10	Summary, practical work with data. Analysis of scientific publication.			Classes		0.50	computer room
11	Independent work with data.			Classes		0.50	computer room
12	Student presentations.			Classes		0.50	computer room
Assessment
Unaided Work:
1. Individual work with the literature – prepare to lectures accordingly to a plan. 2. Individual analysis of a scientific publication. 3. Individual work – each student will receive a research data file (or students can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistic, inferential statistic and/ or analytical statistics methods. As well as to report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture. 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. one A4 page). On completion of this course: 1. Exam, multiple choice test with theoretical questions in statistics (50%). 2. Independent works: oral presentation of individual work and analysis of a scientific publication (50%).
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		Upon completion of this course, students will demonstrate knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know Excel and IBM SPSS Statstics offered data processing tools; * know data processing method criteria; * correctly interpret the most important statistical indicators.
Skills:		Upon completion of this course, students will demonstrate skills to: * input and edit data in computer programs Excel and IBM SPSS Statistics; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., ability to do statistical hypothesis testing, correlation analysis; * statistically analyse research data using computer programs Excel and IBM SPSS Statistics; * create tables and graphs in Excel and IBM SPSS Statistics programs with obtained results; * correctly describe obtained research results.
Competencies:		Upon completion of this course, students will be able to argument and make decisions about statistical data processing methods, use them to achieve research aims, using computer programs Excel and IBM SPSS Statistics, practically use learned statistical basic methods to process research data.
Bibliography
No.	Reference
Required Reading
1	Field A. Discovering Statistics using IBM SPSS Statistics. 2018.
2	Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition, 2020.
3	Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition, 2014.
Additional Reading
1	Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp.