Skip to main content
Māra Grēve

Study Course Description

Course Description Statuss:Approved
Course Description Version:10.00
Study Course Accepted:02.04.2020
Study Course Information
Course Code:SL_001LQF level:Level 7
Credit Points:2.00ECTS:3.00
Branch of Science:Mathematics; Theory of Probability and Mathematical StatisticsTarget Audience:Medicine
Study Course Supervisor
Course Supervisor:Māra Grēve
Study Course Implementer
Structural Unit:Statistical Unit
The Head of Structural Unit:Andrejs Ivanovs
Contacts:Kapseļu iela 23, 2.stāvs, Rīga, +371 67060897, statistikaatrsu[pnkts]lv,
Study Course Planning
Full-Time - 1. Semester No.
Lectures (number)Lecture Length (academic hours)Total Contact Hours of Lectures0
Classes (number)14Class Length (academic hours)3Total Contact Hours of Classes42
Total Contact Hours42
Study course description
Preliminary Knowledge:
Knowledge of mathematics and informatics relevant to the programme of secondary education.
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.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Introduction 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.Classes1.00computer room
2Descriptive statistics in MS Excel and IBM SPSS.Classes1.00computer room
3Descriptive statistics of the Normal distribution. Confidence intervals.Classes1.00computer room
4Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculations.Classes1.00computer room
5Parametric statistics for quantitative data. Comparison of independent and dependent samples.Classes1.00computer room
6Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples.Classes1.00computer room
7Qualitative data processing. Independent and dependent samples.Classes1.00computer room
8Correlation analysis. Regression analysis (Linear regression).Classes1.00computer room
9Regression analysis (Binary logistic regression). ROC curves.Classes1.00computer room
10Survival analysis (Kaplan-Meier estimate and Cox proportional hazards regression model).Classes1.00computer room
11Summary and practical work with data using IBM SPSS.Classes1.00computer room
12Analysis of scientific publications.Classes1.00computer room
13Independent work with data using IBM SPSS.Classes1.00computer room
14Students presentations.Classes1.00computer room
Unaided Work:
1. Individual work with literature – preparation for each class according to the thematic plan. 2. Individual analysis of a scientific publication. 3. Independent work in pairs: each pair will get a research data file (or students can use their own) with previously defined research tasks. Students will process the data to fulfil requirements of the defined tasks using descriptive statistics and inferential statistics, describe the obtained results in a scientifically appropriate way in the final paper and present the results during the last class.
Assessment Criteria:
1. Participation in practical lectures. A practical assignment for each missed class. 2. Oral presentation of the analysis of a scientific publication. 3. Oral presentation of independent work. 4. Exam, where 50% – practical task with a database, 50% – multiple-choice test with 30 theoretical and practical questions in statistics.
Final Examination (Full-Time):Exam
Final Examination (Part-Time):
Learning Outcomes
Knowledge:Upon completion of this course, students will have acquired basic 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: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.
Competencies: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.
1Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal, 2nd edition, John Wiley & Sons, 2014. ISBN-13: 978-1118589939
2Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, Sage Publications, 2013. ISBN-13: 978-1446249185
3Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2019. ISBN: 978-1-119-16781-5