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Biostatistics

Course abstract

Aim of the course is to provide students with knowledge in general statistics, mathematical statistics and applied mathematics and skills, which is needed for other study disciplines, as well as in nutrition education.
The tasks of the course are as follows:
1) to deepen knowledge in general statistics, probability theory and mathematical statistics concepts and methods;

2) to provide knowledge about concepts of correlation, factor and discriminant analysis and their usage.

Learning outcomes and their assessment

Knowledge
After the course, students will be familiar with statistical methods used in different types of publications; will manage the possibilities of data processing using Ms Excel and SPSS; will manage criteria using parametric and non-parametric methods.
Skills
Students will know and be able to
1) create and edit database using MS Excel, SPSS;
2) show the principles of data coding for statistical analysis;
3) create and edit tables, diagrams;
4) process data of measurements and questionnaire using computer (Ms Excel, SPSS);
5) choose correct data processing methods, including statistical hypothesis (testing);
6) correct usage of statistical tests for data analysis.
Competencies
Students will be able to correctly interpret main statistical indicators in their field and practically interpret basic statistical methods in the data analysis and interpretation.

Course Content(Calendar)

Lectures
1. Introduction. Data collection, creation of database. Introduction to SPSS. L1 P1
2. Presentation of data. Descriptive statistics. L2 P2
3. Statistical hypothesis testing. Parametric methods. L3 P3
4. Statistical hypothesis testing. Non-parametric methods. L4, P4
5. Statistical hypothesis testing. Qualitative data. L5 P5
6. Correlation theory elements. Regression analysis. L6 P6
7. The concept of survival analysis. Concept of a factor, discriminant, and cluster analysis. L7 P7
8. Analysis of scientific publication. Analysis of research data project. S8
Practical works
1st practical work. Data collection, creation of database.
2nd practical work. Descriptive statistics.
3rd practical work. Parametric methods.
4th practical work. Non-parametric methods.
5th practical work. Qualitative data.
6th practical work. Regression analysis.
7th practical work. Concept of a factor, discriminant, and cluster analysis.
Seminars

1st seminar. Analysis of research data project.

Requirements for awarding credit points

Participation in seminars and practical work is mandatory.
Student evaluation includes:
1) presentation of scientific research paper analysis – 30%;
2) preparation of statistical analysis plan for master thesis – 20%;

3) examination (written exam) – 25%.

Description of the organization and tasks of students’ independent work

Student’s independent work is done individually by assigning following tasks:
1) independent preparation of practical classes;
2) publication analysis and statistical analysis plan for master thesis;

3) to read the relevant literature regarding themes of the course.

Criteria for Evaluating Learning Outcomes

Student evaluation includes:
1) presentation of scientific research paper analysis – 30%;
2) preparation of statistical analysis plan for master thesis – 20%;

3) examination (written exam) – 25%.

Compulsory reading

1. Teibe, U. (2007) Bioloģiskā statistika. LU Akadēmiskais apgāds.
2. Krastiņš, O. (2003) Statistika. LR Centrālās Statistikas pārvalde.
3. Arhipova, I., Bāliņa, S. (2003) Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Datorzinību centrs.

4. Pētniecība. Teorija un prakse (2016) RaKa.

Further reading

1. Altman, D. (1997) Practical Statistics for Medical Research. Chapman & Hall.
2. Riffenburgh, R.H. (2012) Statistics in Medicine 3rd edit. Elsevier.
3. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal (2014) Edit. B.Barton, J.Peat. 2nd edit. Wiley Blackwell.

4. Field, A. (2009) Discovering Statistics Using SPSS. Sage.

Periodicals and other sources

1. Latvijas statistikas gadagrāmata
2. Latvijas Centrālā Statistikas biroja dati http://www.csb.gov.lv
3. http://www.lib.gla.ac.uk/Depts/MOPS/stats/medstats.html

4. SPSS for Beginners. http://www.spss.com