Biostatistics
Study Course Implementer
14 Balozu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
Preliminary Knowledge
Learning Outcomes
Knowledge
1.After completion of this course, the student will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know commonly used data processing tools in MS Excel and IBM SPSS; * know data processing criteria of various statistical methods; * interpret correctly the most important statistical indicators.
Skills
1.After completion of this course, the student will demonstrate skills to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., will be able to do statistical hypothesis testing; * statistically analyse research data using computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programmes with obtained results; * describe obtained research results correclty.
Competences
1.After 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 programs MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data.
Assessment
Individual work
Examination
Study Course Theme Plan
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Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program.
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Descriptive statistics.
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Descriptive statistics of the Normal distribution. Confidence intervals.
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Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculation. Qualitative data processing. Independent and dependent samples.
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Parametric statistics for quantitative data. Comparison of independent and depentend samples.
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Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples.
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Correlation analysis. Regression analysis (Linear regression).
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Regression analysis (Binary logistic regression). ROC curves.
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Summary and practical work with data using IBM SPSS.
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Analysis of scientific publications.
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Independent work with data using IBM SPSS.
Bibliography
Required Reading
Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition. John Wiley & Sons, 2014.
Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition. Sage Publications, 2018.
Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition. Wiley-Blackwell, 2020.