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Lecturer(s)
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Dostál Daniel, PhDr. Ph.D.
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Course content
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The course consists of the following areas: 1. data matrix, statistical hypothesis tests I 2. statistical hypothesis tests II, indicators of effect si 3. parametric tests I (t-test, Welch test) 4. parametric tests II (F test, Pearson's correlation coefficient tests) 5. analysis of variance, the multiple testing issue, post-hoc tests 6. assumptions of parametric tests and their validation 7. power analysis 8. pivot tables, goodness of fit tests 9. nonparametric tests I 10. nonparametric tests II (Kruskal-Wallis test, Spearman correlation coefficient) 11. selection of statistical test
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Work with Text (with Book, Textbook), Demonstration, Projection (static, dynamic)
- Homework for Teaching
- 35 hours per semester
- Preparation for the Exam
- 40 hours per semester
- Attendace
- 25 hours per semester
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Learning outcomes
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The main topic the course are statistical hypothesis tests. Students will understand the logic of inferential statistics and the principle of basic statistical tests. In addition to statistical significance and the concept of practical significance and power of a statistical test will be introduced. Finally, students will get familiar with the weaknesses and limitations of the null hypothesis testing in research practice. Acquired skills will be practiced in statistical software STATISTICA 13.3.
By completing this course, students will gain the ability to formulate a statistical hypothesis, select and perform the appropriate statistical test, and interpret the results. Furthermore, students understand the concepts of the effect size, statistical power, power analysis etc.
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Prerequisites
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Knowledge of the basics of probability theory and mathematical statistics, descriptive statistics and principles statistical estimates. Recommended (although not necessarily) is a pre-requisite course Statistical Methods in Psychology 1.
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Assessment methods and criteria
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Mark, Oral exam, Written exam, Student performance
The course requires a credit and a examination. In order to receive credit, students must complete homework consisting of individual work with data. At least 80% of the points are required for successful completion. The exam is computer-based and again consists of individual work with data (mainly the selection, evaluation and interpretation of a statistical test). The test consists of eight tasks, which will always (or almost always) relate to the assigned data table. The tasks will be arranged in four pairs. The first task of the pair consists of selecting, performing and interpreting the test. The second task of each pair will involve some more advanced knowledge (e.g., calculating the effect size indicator, analyzing the power of the test, calculating the p-value from a given distribution, comparing two sample correlation coefficients, converting a two-sided p-value to a one-sided p-value...). If a student solves all 8 problems correctly, they receive a grade of A. For each problem incorrectly solved, the grade is reduced by one grade.
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Recommended literature
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Daniel Dostál. Statistické metody v psychologii.
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