Biostatistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:16.00

Study Course Accepted:26.08.2024 16:16:17

Study Course Information
Course Code:		SL_004		LQF level:		Level 7
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Dentistry
Study Course Supervisor
Course Supervisor:		Madara Miķelsone
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		14 Balozu 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)		11	Class Length (academic hours)		3	Total Contact Hours of Classes		33
Total Contact Hours								33
Study course description
Preliminary Knowledge:
Secondary school background in mathematics and informatics.
Objective:
To get basic knowledge and skills in data processing methods (descriptive statistics, methods of inferential statistics to estimate differences between groups and relationships between variables), to use in scientific work.
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. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program.			Classes		1.00	computer room
2	Descriptive statistics.			Classes		1.00	computer room
3	Descriptive statistics of the Normal distribution. Confidence intervals.			Classes		1.00	computer room
4	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculation. Qualitative data processing. Independent and dependent samples.			Classes		1.00	computer room
5	Parametric statistics for quantitative data. Comparison of independent and depentend samples.			Classes		1.00	computer room
6	Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples.			Classes		1.00	computer room
7	Correlation analysis. Regression analysis (Linear regression).			Classes		1.00	computer room
8	Regression analysis (Binary logistic regression). ROC curves.			Classes		1.00	computer room
9	Summary and practical work with data using IBM SPSS.			Classes		1.00	computer room
10	Analysis of scientific publications.			Classes		1.00	computer room
11	Independent work with data using IBM SPSS.			Classes		1.00	computer room
Assessment
Unaided Work:
1. Individual work with literature – preparation for each class according to the thematic plan. 2. Individual analysis of a scientific publication - each student will search for one full text scientific publication where data analysis methods included in this course is used. After finding and reading the publication, student will give 5 to 7 minute presentation about the use of statistical methods, results and formulation of conclusions in it. 3. Independent work - each student will have to complete four tasks, including closed and open-ended questions about descriptive statistics and inferential statistics. After successfully accomplishing this course, please fill out the study course evaluation form to give us feedback, we will appreciate that a lot!
Assessment Criteria:
For successful integration of knowledge and to prepare for the final exam, the student performs the following activities (mandatory, not graded): 1. Participation in practical lectures. For each missed class, student must attend a session with another group under the current lecturer or study the topic independently and completing the test yourself questions in e-studies. 2. Oral presentation of the analysis of a scientific publication. The grade of the course is cumulative, where: 50% – exam - independent work. 50% – multiple-choice test with 30 theoretical and practical questions in statistics with a time limit of 45 minutes.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):
Learning Outcomes
Knowledge:		After completion of this course, the student will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know commonly used data processing tools in MS Excel and IBM SPSS; * know data processing criteria of various statistical methods; * interpret correctly the most important statistical indicators.
Skills:		After completion of this course, the student will demonstrate skills to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., will be able to do statistical hypothesis testing; * statistically analyse research data using computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programmes with obtained results; * describe obtained research results correclty.
Competencies:		After 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 MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data.
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. 4th edition. Sage Publications, 2018.
3	Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition. Wiley-Blackwell, 2020.
4	Grech, V. Write a Scientific Paper (WASP): Effective graphs and tables. Early Human Development, 2019. 134, 51-54. DOI: 10.1016/j.earlhumdev.2019.05.013.