Veidlapa Nr. M-3 (8)
Study Course Description
Basics of Biostatistics
Course Code
SL_013
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Rehabilitation
LQF
Level 6
Study Type And Form
Full-Time; Part-Time
Study Course Implementer
Course Supervisor
Ināra Kantāne
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
Baložu Street 14, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To get basic knowledge in data processing methods (descriptive statistics, inferential statistics to estimate differences), that can be used in bachelor's paper, analysis of scientific literature and research work in their specialty.
Preliminary Knowledge
Secondary school knowledge in Mathematics and Informatics.
Learning Outcomes
Knowledge
1.Upon completion of this course, students will demonstrate knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know IBM SPSS Statstics offered data processing tools; * know data processing method criteria; * correctly interpret the most important statistical indicators.
Skills
1.Upon completion of this course, students will demonstrate skills to: * input and edit data in computer programs Excel and IBM SPSS Statistics; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., ability to do statistical hypothesis testing, correlation analysis; * statistically analyse research data using computer program IBM SPSS Statistics; * create tables and graphs in IBM SPSS Statistics programs with obtained results; * correctly describe obtained research results.
Competences
1.Upon completion of this course, students will be able to argument and make decisions about statistical data processing methods, use them to achieve research aims, using computer program IBM SPSS Statistics, practically use learned statistical basic methods to process research data.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
1. Individual work with literature, according to the thematic plan. 2. Independent analysis of the scientific publication. 3. Final Paper – a study data file will be prepared for each student (or the student can use the data of their study) with the study tasks defined. The student will need to process the data statistically to achieve the defined tasks, describe the results obtained, present the results obtained in the last lesson. In order to evaluate the quality of the study course as a whole, the student must complete the study course evaluation questionnaire on the student portal. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
10 points
|
|
Participation in practical classes. For each delayed lesson, the written work assigned by the lecturer must be performed. At the end of the study course: 1. Exam (written), multi-answer test on subjects covered in the course, representing 50% of the final assessment. 2. Independent work: oral presentation of the final Paper and analysis of the scientific publication, comprising 50% of the final assessment. |
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Descriptive statistics in MS Excel and IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Introduction to IBM SPSS Statistics. Basic actions with data in the IBM SPSS Statistics program.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Descriptive statistics of the Normal distribution.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Statistical hypothesis, types of statistical hypothesis.
Hypothesis testing. P value.
Dependent and independent samples.
Parametric and nonparametric data processing methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Parametric statistics for quantitative data.
Comparison of independent samples and dependent samples (t test, Analysis of Variance).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Nonparametric statistics for quantitative data.
Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis test).
Comparison of dependent samples (Wilcoxon test, Friedman test).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Qualitative data processing. Pearson chi square test, Fisher's exact test, McNemar's test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Correlation analysis. Reliability analysis. Internal consistency measure (Cronbach's alpha).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Summary, practical work with data. Analysis of scientific publication.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Independent work with data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Student presentations.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
33 Academic Hours
Final Examination:
Exam (Written)
PART-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 preparation in Excel.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Descriptive statistics of the Normal distribution.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Statistical hypothesis, types of statistical hypothesis.
Hypothesis testing. P value.
Dependent and independent samples.
Parametric and nonparametric data processing methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Parametric statistics for quantitative data.
Comparison of independent samples and dependent samples (t test, Analysis of Variance).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Nonparametric statistics for quantitative data.
Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis test).
Comparison of dependent samples (Wilcoxon test, Friedman test).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Qualitative data processing. Pearson chi square test, Fisher's exact test, McNemar's test.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Correlation analysis. Reliability analysis. Internal consistency measure (Cronbach's alpha).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Summary, practical work with data. Analysis of scientific publication.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Student presentations.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
27 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Field A. Discovering Statistics using IBM SPSS Statistics. 2018.
2.
Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition, 2020.
3.
Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition, 2014.
Additional Reading
1.
Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp.