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Lecturer(s)
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Dvořáčková Olga, Mgr. Ph.D.
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Course content
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Covered topics: 1.Descriptive statistics (incl. graphical representation) of various data types 2. Principles of hypotheses testing. Statistical significance 3. Chi-square test 4. Contingency tables 5. t-tests 6. ANOVA, post-hoc tests 7. Nonparametric tests 8. Correlation and regression 9. Multidimensional analyses (Factor analysis, Correspondence analysis, Discriminant analysis) 10. Recapitulation
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, E-learning, Individual tutoring
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Learning outcomes
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The course is building on the basic methodology knowledge from the bachelor level. It focuses mainly on the more advanced techniques of data evaluation. The goal is to master the skills to acquire and analyze research data at a level needed for a diploma thesis.
Students will be able to design an experimental survey, collect and analyze data and present their findings.
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Prerequisites
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The course is intended for graduate students - some basic level of statistical knowledge is expected. Students are assumed to have a basic knowledge of MS Excel or its alternatives.
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Assessment methods and criteria
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Oral examination, Written examination, Analysis of student's work activities (technical works)
Students ought to process the supplied data using any software available (MS Excel, IBM SPSS). Second choice is an oral examination.
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Recommended literature
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CARIFIO J., PERLA R. J. Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes. Journal of Social Sciences, 3(3): 106-116. 2007.
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FIELD A. Discovering Statistics Using SPSS. 3. ed. London: Sage Publications, 2009. ISBN 978-1-84787-906-6.
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OSGOOD C. E., SUCI G. J., TANNENBAUM P. H. The Measurement of Meaning. University of Illinois Press. Urbana, 1957. ISBN 0-252-74539-6.
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