Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.P.3.00

Description Period: 2025./26 academic year pavasara semestris

Study Course Accepted: -

Study Course Description

Biostatistics

Main Study Course Information

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; * 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 program 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 program IBM SPSS; * create tables and graphs 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 program IBM SPSS; to use the acquired basic statistical methods in processing research data.

Assessment

Individual work

Title
% from total grade
Grade
1.

Attendance
-
-

Participation in practical classes is compulsory. For each class missed, the student must either attend a session with another group under the same lecturer, or study the topic independently and complete the 'Test Yourself' section on the respective topic in E-studies.

Examination

Title
% from total grade
Grade
1.

Assessment
-
10 points

The final grade is cumulative and is calculated as the weighted average of the following components:

  • Three multiple-choice question (MCQ) tests, each consisting of ten short questions (time limit: 10 minutes), performed in class in a safe browser environment after completion of three large themes (MCQ-1 covers topics 1–3, MCQ-2 covers topics 4–6, and MCQ-3 covers topics 7–9);
  • Individual independent work (analysis of a scientific publication), prepared according to the guidelines and uploaded to the e-studies prior to the respective class in which it is presented;
  • Scenario analysis test (time limit: 2 hours), performed in class in a safe browser environment. This test involves solving 4 statistical problems (scenarios). Each scenario is followed by 10–12 questions, including both multiple-choice and open-ended questions;
  • Exam, which is a practical test for data analysis using the IBM SPSS statistical programme. This is performed in class and has a time limit of 2 hours.

The assessment for each component is first calculated as a percentage. The weighted average percentage is then calculated.

For the calcultion of the weighted average each of the MCQ tests and the publication analysis have a weight of 1, while the scenario analysis test and exam each have the weight of 3. The weighted average is calculated in percents and then converted into the 10-point scale.

Each of the tests can be taken only once; there is no minimum passing level on any of the components. All components must be completed. If, after completing the three multiple-choice tests, the publication analysis and the scenario test, a student has achieved a passing score (i.e. over 55%), they can choose not show up for the exam. In this case, the exam will be scored as 0%. However, they must inform the teacher of their decision at least one day prior to the scheduled exam.

If the cumulative grade is insufficient (i.e. the weighted average is below 55%), an online session in the form of an individual interview with at least two teachers is organised for the student on the topic(s) in which they had the lowest results. If the student demonstrates satisfactory progress, they will receive the lowest passing grade ("4").

Study Course Theme Plan

FULL-TIME
Part 1

  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 and preparation for statistical analysis. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Descriptive statistics. Normal distribution and its descriptive statistics. Confidence intervals.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P-value. Related and independent samples. Sample size calculation.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Parametric statistics for quantitative data. Comparison of independent and related samples.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Nonparametric statistics. Comparison of independent and related samples.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Qualitative data processing. Independent and dependent samples.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Correlation analysis.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Regression analysis (Linear regression).

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Basic principles of survival analysis.

  1. Class/Seminar

Modality
Location
Contact hours
Off site
Computer room
3

Topics

Regression analysis (Binary logistic regression). ROC curves.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Scenario (situation) analysis. Choosing the right statistical methods and interpreting the results.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
3

Topics

Analysis of scientific publications.
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.Suitable for English stream