Veidlapa Nr. M-3 (8)
Study Course Description
Biostatistics
Course Code
SL_003
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
6.00
Target Audience
Life Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Māra Grēve
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
23 Kapselu street, 2nd floor, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To acquire knowledge and skills in the statistical data processing methods used in biomedicine (descriptive statistics, inferential statistics for assessing differences and exploring relationships between different variables), which are necessary for the development of a scientific research thesis at Master's level, for an in-depth analysis of scientific publications and for the correct recording of the results of quantitative research.
Preliminary Knowledge
Knowledge in mathematics and informatics corresponding to the level of secondary education. Basic knowledge of statistics and research design is preferred.
Learning Outcomes
Knowledge
1.Upon successful completion of the course, students will have acquired knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know the most often used possibilities offered by MS Excel and IBM SPSS in data processing; * know the criteria for using data processing methods; * interpret the most important statistical indicators correctly.
Skills
1.After completion of this course, students will be able to: *enter and edit data in computer programs MS Excel and IBM SPSS; *prepare data for statistical processing correctly; *choose appropriate data processing methods, incl., statistical hypothesis testing; *process research data statistically using computer programs MS Excel and IBM SPSS; *create tables and charts for the results obtained by MS Excel and IBM SPSS programs; *describe the obtained research results precisely.
Competences
1.After completion of this course, students will be able to make reasoned decisions about the use of statistical data processing methods to achieve research aims, and using computer programs MS Excel and IBM SPSS, practically apply the learned basic statistical methods in research data processing.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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1. Individual work with the literature - preparation for each lesson, according to the thematic plan, using lecture presentations and recommended literature.
2. Independent creation of a table for comparison of two independent groups, designing it according to the requirements for publications - the student will be given data (various types of variables), which must be analysed as in the previously learned topics and the results must be summarised in a table, an example of which will be given.
3. Independent analysis of a scientific publication - the student is required to find a publication (reliable scientific literature) on a biomedical topic of interest to him/her, which uses one of the statistical methods of data processing taught in the course, present it and engage in a discussion about the scientific publications chosen by other students.
4. Independent work - the student will be provided with research data files (or students may use their own research data) with defined research tasks. The student will be required to process the data to achieve the defined tasks using descriptive statistical and inferential statistical methods, describe the results obtained in the final paper, design the paper according to the requirements and present the obtained results in the last class.
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.
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Examination
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Title
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% from total grade
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Grade
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|---|---|---|
|
1.
Examination |
-
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-
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Participation in practical classes is mandatory, without assessment. For every missed class - a practical assignment.
Mark - cumulative.
1. Individual practical task with IBM SPSS Statistics - 50%;
2. Two multiple-choice tests (15 theoretical and practical questions in each with a time limit of 15 minutes) - 50%.
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Study Course Theme Plan
FULL-TIME
Part 1
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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4
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Topics
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Introduction to statistics, the role of statistics in research process.
Data types, measuring scales, data entry, data preparation in MS Excel.
Introduction to IBM SPSS. Basic operations with data in IBM SPSS. Calculation and graphical representation of frequency distributions.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Indicators of descriptive statistics in MS Excel and IBM SPSS, their graphical representation.
Normal distribution and its characteristic descriptive statistics indicators.
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-
Lecture
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Confidence intervals. Statistical hypotheses, their types. Hypothesis testing. P value. Normal distribution test tests for IBM SPSS. One sample t-test in Ms Excel and IBM SPSS. Sample size calculation.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Parametric and non-parametric data processing methods. Comparison of independent and dependent samples for two groups.
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-
Lecture
|
Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
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4
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Topics
|
Parametric and non-parametric data processing methods. Comparing independent and dependent samples for more than two groups.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Qualitative data processing methods for independent and dependent samples. Independent work: summary of a comparison of two independent groups.
|
-
Lecture
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Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
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Topics
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Correlation analysis and linear regression analysis.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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4
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Topics
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Binary logistic regression. ROC curves.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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4
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Topics
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Survival analysis (Kaplan-Meier method and Cox regression).
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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4
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Topics
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Bland-Altman diagram. Interclass correlation coefficient. Summary and practical application of the statistical methods learned.
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-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Independent work: analysis of scientific publications.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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4
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Topics
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Independent work with data in IBM SPSS. Presentation of independent work.
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Total ECTS (Creditpoints):
6.00
Contact hours:
48 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Petrie, A. & Sabin, C. Medical Statistics at a Glance. 4th edition, Wiley-Blackwell, 2020.
2.
Peat, J. & Barton, B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition, John Wiley & Sons, 2014.
3.
Field, A. Discovering Statistics using IBM SPSS Statistics. 5th edition, Sage Publications, 2018.
Additional Reading
1.
Grech, V. Write a Scientific Paper (WASP): Effective graphs and tables. Early Human Development, 2019. DOI: 10.1016/j.earlhumdev.2019.05.013