Veidlapa Nr. M-3 (8)
Study Course Description
Biostatistics
Course Code
SL_001
Branch of Science
Health sciences; Other Sub-Branches of Health and Sports Science
ECTS
3.00
Target Audience
Medicine
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Olga Rajevska
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
14 Balozu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To acquire basic knowledge and skills in statistical data processing methods (descriptive statistics, methods of inferential statistics to estimate differences between groups and relationships between variables) required for the development of research work and the application of statistical indicators in their specialty.
Preliminary Knowledge
Knowledge of mathematics and informatics relevant to the programme of secondary education.
Learning Outcomes
Knowledge
1.Upon completion of this course, students will have acquired knowledge that will allow to: * recognise terminology used in statistics and basic methods used in different types of publications; * be competent in commonly used data processing tools in MS Excel and IBM SPSS; * be aware of data processing criteria for various statistical methods; * interpret the most important statistical indicators accurately.
Skills
1.Upon completion of this course, students will be able to: * enter and edit data in the computer programs MS Excel and IBM SPSS; * correctly prepare data for statistical processing and analysis; * choose appropriate data processing methods, including the ability to do statistical hypothesis tests; * statistically process research data using the computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programs for the obtained results; * describe the obtained research results correctly.
Competences
1.Upon completion of this course, students will be able to take an informed decision about the use of statistical data processing methods to achieve research aims, using the computer programs MS Excel and IBM SPSS; to use the acquired basic statistical methods in processing research data.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual 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.
A. After successfully accomplishing this course, please fill out the study course evaluation form to give us feedback, we will appreciate that a lot!
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
10 points
|
|
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. |
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
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.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Descriptive statistics. Sample size calculation.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value.
Descriptive statistics of the Normal distribution.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Parametric statistics for quantitative data.
Comparison of independent and dependent samples.
Confidence intervals.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Nonparametric statistics.
Comparison of independent and dependent samples.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Qualitative data processing. Independent and dependent samples.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Correlation analysis. Regression analysis (Linear regression).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Regression analysis (Binary logistic regression). ROC curves.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Survival analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
Computer room
|
3
|
Topics
|
Analysis of scientific publications.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Practical work with data using IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Independent work with data using IBM SPSS.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
36 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition. John Wiley & Sons, 2014. (pēdējais iznākušais izdevums)Suitable for English stream
2.
Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition. Sage Publications, 2024.Suitable for English stream
3.
Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition. Wiley-Blackwell, 2019.Suitable for English stream
4.
Grech, V. Write a Scientific Paper (WASP): Effective graphs and tables. Early Human Development, 2019. 134, 51-54. DOI: 10.1016/j.earlhumdev.2019.05.013Suitable for English stream