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Lecturer(s)
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Hricová Alena, doc. PhDr. Bc. Ph.D.
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Mrhálek Tomáš, Mgr. Ph.D.
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Course content
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The courses run in the form of practicing specific statistical analysis that considers the data characteristics of students. The courses will be realized through practical training in the SPSS programme (PC lab). The students will especially learn: - testing of normality of data allocation - parametric and non-parametric statistical tests - contingency tables - qui quadrate teat - t-tests - correlation - dispersion analysis - regression analysis - multidimensional analysis The course will include personal consultation of the selected methods.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration
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Learning outcomes
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The aim of the course is for the students to achieve such knowledge and skills in the statistical data methodology that they will be able to elaborate the statistics of their dissertation thesis on their own.
A student is able to assess the data of their dissertation thesis statistically by themselves.
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Prerequisites
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Knowledge of basic methodology of socioscientific research (questionnaire formation, formulation of hypothesis etc.)
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Assessment methods and criteria
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Student performance assessment
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Recommended literature
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FIELD, A. Discovering statistic using spss. Sage, 2013.
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Hendl, J. Přehled statistických metod zpracování dat.. Praha: Portál, 2006. ISBN 80-7367-123-9.
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