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Lecturer(s)
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Ťoupal Tomáš, Ing. Ph.D.
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Šedivá Blanka, RNDr. Ph.D.
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Course content
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1. Basic terminology in scientific work, types of research, scientific methods, and the use of quantitative research methods and statistics in the scientific-research work of a physiotherapist. 2. Ethical principles in scientific work, phases of research, working with information sources, and evidence-based practice. Ethical principles and principles of scientific and research work (including publication ethics). Basic principles of bioethics. 3. Related documents and legislative framework in the process of approval of the research plan by the ethics committee. Clinical data anonymization procedures. 4. Data file structure for statistical analysis. Quantification and stratification of statistical features. Data typology and categorization according to the degree of quantification. 5. Determination of the research group, data collection techniques, determination of objectives, research questions, and hypotheses. Reliability and validity of the research instrument based on the empirical characteristics of descriptive statistics. Selection errors. 6. Descriptive statistics - calculation of the basic characteristics of a data file, data visualization. 7. Principles of inferential statistics and formulation of statistical hypotheses. A generally valid statistical hypothesis testing procedure. Confidence level, confidence intervals. Principles of probability (random phenomenon), p-value. Interpretation of statistical testing results. Procedures for analyzing the power of a statistical test. 8. Conditions for choosing and using correct statistical tests (parametric/non-parametric) to verify the validity of statistical hypotheses. Normality tests. Tests of test assumptions. 9. Testing the equivalence of mean values for two sample sets, parametric (t-test) and non-parametric approaches based on data obtained from the research activity of a physiotherapist. Prerequisites for the use of individual tests. Interpretation of test results. Paired t-tests. 10. Testing the equivalence of means for multiple sample sets, parametric (ANOVA) and non-parametric approaches. Prerequisites for the use of individual tests. Interpretation of test results. 11. Testing the equivalence of variability for two sample sets, parametric and non-parametric approaches. Prerequisites for the use of individual tests. Interpretation of test results. Examples of the use of these tests on data obtained by research activity. 12. Statistical testing of connections, testing of dependence and independence in contingency tables. Basic principles of correlation models, the problem of apparent correlations. Causality and correlation in biomedical data. 13. Formulation of regression models for multi-variable dependency models, methods of estimation of model parameters, interpretation of results. Sensitivity analysis of model estimates to outliers. 14. Problems of analyzing the structure of multidimensional data sets, principles of data dimensionality reduction, methods of factor analysis. Identification of predictors based on statistical analysis of the ensemble. Cluster analysis and its use on data obtained by the research activity of a physiotherapist. 15. Suitability of applying different statistical methods to different types of data. Overview of other statistical methods and procedures. Processing of large data sets. Calculations of epidemiological models. Seminars - thematically follow the topics of the lectures with practical application.
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Learning activities and teaching methods
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Lecture supplemented with a discussion, Lecture with practical applications, Individual study, Lecture
- Contact hours
- 60 hours per semester
- Presentation preparation (report) (1-10)
- 8 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Preparation for comprehensive test (10-40)
- 20 hours per semester
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| prerequisite |
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| Knowledge |
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| Students should have practical experience with office suite applications. |
| Skills |
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| Ovládat na uživatelské úrovni program Excel. |
| Competences |
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| N/A |
| N/A |
| mgr. studium: kriticky přistupuje ke zdrojům informací, informace tvořivě zpracovává a využívá při svém studiu a praxi |
| learning outcomes |
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| Knowledge |
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| Learning outcomes: On completion of this module the student will be able to: - describe the statistical SW applicable for statistical data processing; - review fitness SW for choice statistical problems; - illustrate using SW on simple examples; - use select SW for statistical data processing; - apply statistical principles on real problems and suggest their solving in SW; - interpret the statistic results. |
| Skills |
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| - znát statistické funkce v sw Excel (případně v dalších statisticky orientovaných softwarech) - aplikovat teoretické poznatky z oblasti pravděpodobnosti v SW Excel (případně v dalších statisticky orientovaných softwarech) - využívat znalosti základních statistických metod a postupů pro analýzu dat v sw Ecxel (případně v dalších statisticky orientovaných softwarech) - aplikovat statistické principy na vybrané reálné problémy a navrhnout jejich řešení ve zvoleném SW prostředí |
| Competences |
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| N/A |
| teaching methods |
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| Knowledge |
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| Individual study |
| Interactive lecture |
| Skills |
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| Interactive lecture |
| Individual study |
| Competences |
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| Individual study |
| Interactive lecture |
| assessment methods |
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| Knowledge |
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| Combined exam |
| Seminar work |
| Skills |
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| Combined exam |
| Seminar work |
| Competences |
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| Combined exam |
| Seminar work |
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Recommended literature
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Dupont, William D. Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data. Cambridge : Cambridge University Press, 2002. ISBN 0-521-65578-1.
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HENDL J. Přehled statistických metod zpracování. PRAHA, 2006. ISBN 80 7367-123-9.
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Rosner, Bernard. Fundamentals of biostatistics. 8th edition. 2016. ISBN 978-1-305-26892-0.
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ZVÁROVÁ, Jana. Základy statistiky pro biomedicínské obory. 2., dopl. vyd.. Praha: Karolinum, 2011. ISBN 978-80-246-1931-6.
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