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Biostatistics in Food Hygiene

Course developer

Course abstract

Biostatistics in Food Hygiene course at Food hygiene master's program prepares the students for selected research project. During the course sequentially theoretical topics are covered as well as by using examples from the hygiene and health studies skills are trained necessary for masters project planning, data acquisition, retention and structured analysis. Topics about the research results presentation in tabular and graphical format are included. The theoretical background for results generalization and practical application of them are explained.

Learning outcomes and their assessment

Knowledge to explain biostatistics aspects of research results, and their generalization toward conclusion. Knowledge about methods to calculate necessary minimum sample size and to collect sample objects as well as theoretical background for making comparison of statistical indices – 3 independent works;
Skills to format results of research project in a spreadsheet, to run applications for descriptive statistics and chart generation in a regular calculation software. Is able to assist programmed data analysis done by professional software - practicals;
Competence to perform independently data processing of master`s level research project, interpret the obtained results, formulate conclusions and substantiate decisions – master`s research project data acquisition, processing and results demonstaration in charts and tables.

Course Content(Calendar)

1. Structured approach to data analyzes. Structure of primary data table, saving data and keeping them safe. 1 h lecture 2 h practical work.
2. Variable formats often used in hygiene or health studies. Coding of data. Transforming codes. 1 h lecture 2 h practical work.
3. Interactive data analyzes (MS Excell, OpenOffice Calc) and programmed analyzes with list of commands ( STATA, R etc.). 1 h lecture 2 h practical work.
4. Descriptive statistics of variables. Mean values and variance characteristics. Distribution of data. 1 h lecture 2 h practical work. 1st independent work.
5. Hypothesis tests. Parametric and nonparametric methods. Likelihood ratio test. 1 h lecture 2 h practical work.
6. One-way and two-way table for presentation of results. 1 h lecture 2 h practical work.
7. Chart types: circle, bar, box. Mean value and confidence interval. 1 h lecture 2 h practical work.
8. Format of figures and tables inside text file or presentation slide. 1 h lecture 2 h practical work. 2nd independent work.
9. Compare the means. Independent or paired sample. ANOVA and t-test. Repeated measurements, panel data, time-series of data. 1 h lecture 2 h practical work.
10. Outcome or response variables. Predictor or explanatory variables. 1 h lecture 2 h practical work.
11. Assessment of the effect from some factor. Metric response. Binary response. 1 h lecture 2 h practical work.
12. Relative risk. Odds ratio. 1 h lecture 2 h practical work.
13. Compare effects. Controlling the effect, stratification of dataset. 1 h lecture 2 h practical work.
14. Correlation. Linear regression. 1 h lecture 2 h practical work. 3rd independent work.
15. Logistic regression. 1 h lecture 2 h practical work.
16. Interaction, confounding and influence from not observed factors. 1 h lecture 2 h practical work.

Requirements for awarding credit points

Pass with a successful grade.

Description of the organization and tasks of students’ independent work

Student prepares the task independently at their computer under file name “BPH_1_NameSurname”.
1st independent work – primary data in table format, format of variables, coding, descriptive statistics; 2nd independent work – hypothesis formulation and hypothesis tests, presentation of mean value and standard error in chart or table;
3rd independent work – correlation, estimation of the effect from some factor, linear regression.

Criteria for Evaluating Learning Outcomes

Three independent works during semester. Students have to upload their work at e-study platform. Evaluation according to 10 point scale indicated at the LLU Study Regulations. Final assessment of the study course is calculated as average of three marks from independent works.

Compulsory reading

1. Hills M., De Stavola B.L. A Short Introduction to STATA for Biostatistics. London: Timberlake Consultants, 2009. 188 p. 2. Hirsch R.P. Introduction to Biostatistical Applications in Health Research with Microsoft Office Excell. Wiley, 2016. 408 p. 3. Altman D.G. Practical Statistical for Medical Research. London: Chapman & Hall/CRC, 1991. 611 p.

Further reading

1. Krastiņš O., Ciemiņa I. Statistika. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp. 2. Dohoo I., Martin W., Stryhn H. Veterinary Epidemiologic Research. Charlottetown: VER inc., 2010. 865 p. 3. Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007. 156 lpp.

Notes

Compulsory course of the master`s study programme Food Hygiene.