Veidlapa Nr. M-3 (8)
Study Course Description
Biostatistics
Course Code
SL_007
Branch of Science
Mathematics
ECTS
3.00
Target Audience
Clinical Pharmacy
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
14 Balozu street, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To provide students with the opportunity to acquire basic knowledge and skills in statistical data processing methods (descriptive statistics, inferential statistics methods for evaluating differences and analytical statistics), which are necessary for the development of scientific research work and the application of statistical indicators in their specialty.
Preliminary Knowledge
Secondary school knowledge in mathematics and informatics.
Learning Outcomes
Knowledge
1.After successfully accomplished study course, students will have acquired knowledge to: Correctly interpret the main statistical tests; Describe measurement results using statistical parameters.
Skills
1.Will be able to define the hypotheses of basic statistical tests; Will be able to draw a normal distribution and calculate its main characteristic parameters; Will be able to calculate Pearson and Spearman correlation coefficients; Will be able to calculate and analyze the regression equation; Will be able to calculate independent and dependent sample t-tests; Will be able to perform Pearson's chi-square and Fisher's exact test; Will know how to use the processing program IBM SPSS for data processing and visualization; Will be able to assess the conformity of quantitative data with the existence of a normal distribution; Will be able to perform analysis of variance (ANOVA); Will be able to perform non-parametric tests - Mann-Whitney, Wilcoxon, Friedman and Kruskal-Wallis; Will know how to perform Kaplan-Meier survival analysis; Will be able to operate in the IBM SPSS computer program environment with data selection and perform the necessary calculations; Will be able to formulate the necessary statistical tests for analysis; analyze your own data; will be able to adequately process them and draw consequential and justified conclusions.
Competences
1.As a result of learning the study course, students will be able to independently perform basic operations in the IBM SPSS environment, performing data processing, visualization and the necessary calculations.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
1. Individual work with the literature – prepare for lectures accordingly to the plan. 2. Individual analysis of scientific publication. 3. Individual work – every student will receive a research data file (or the student can use their own) with previously defined research tasks. Students will analyze data using descriptive statistics and inferential statistics, and report the obtained results in a final paper, using a defined formatting style. 4. Two independent works covering course topics - descriptive statistics and statisctical tests used for data analysis. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
10 points
|
|
Participation in practical lectures. Scientific publication analysis, Recognise terminology used in statistics and basic methods used in different publications. To get a successful mark: 1. Multiple choice test about statistics – 30%, 2. Scientific publication analysis – 10%, 3. Individual work with data – 40%, 4. Independent work on descriptive statistics - 10%, 5. Independent work on tests for data analysis - 10%. |
||
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.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Descriptive statistics for quantitative and qualitative data.
Descriptive statistics of the Normal distribution.
Creation of tables and diagrams, correct design.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Hypothesis testing. Parametric and nonparametric tests for quantitative data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Hypothesis testing. Tests for qualitative data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Correlation analysis in MS Excel and IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Regression analysis. ROC curves. Survival analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Sample size estimation (including clinical trials).
Analysis of scientific publications.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Independent work with data.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007, p 155. (akceptējams izdevums)
2.
Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
Additional Reading
1.
Altman D. Practical Statistics for Medical Research. Chapman & Hall, 1999, pp. 612.