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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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During the course, students will be introduced to the following methods: - Advanced skills in building regression models (non-linear relationships, nominal regressors, interactions) - Mixed effects models - Hotelling test, MANOVA and MANCOVA - Log-normal regression - Logistic regression - Poisson regression - Cluster analysis (k-means method and hierarchical clustering) - Analysis of canonical correlations - Other methods according to student and lecturer interest. The projects will expose students to other procedures, many of which they are familiar with from other courses. For example: - Various linear regression models - Exploratory factor analysis and principal components analysis - Structural modelling and confirmatory factor analysis - IRT models, DIF analysis - ROC analysis
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Demonstration, Projection (static, dynamic)
- Homework for Teaching
- 30 hours per semester
- Preparation for the Exam
- 20 hours per semester
- Attendace
- 4 hours per semester
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Learning outcomes
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The aim of the course is to introduce students to various methods of statistical analysis of multivariate data. During the semester, students will encounter specific projects where the various methods have been applied and they will apply them to their own data.
Students will gain an overview of multivariate data analysis methods and try out some of the procedures themselves.
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Prerequisites
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Knowledge of descriptive and inferential statistics. Orientation in terms of linear models (regression analysis).
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Assessment methods and criteria
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Oral exam, Student performance, Seminar Work
Students are assessed by a grade derived from the number of points they have earned during the semester. Points can be earned in two ways: 1) By submitting a report documenting the use of a multivariate statistical method. 2) By evaluating the reports of their classmates. The semester is divided into 4 periods. Students may (but are not required to) submit one report in each period. Each report submitted is scored from 0 to 4 points. The number of points a student receives for a report depends on how the report was evaluated by other students and possibly also by the lecturer. The reports are evaluated in terms of their level of complexity (sophistication, originality, correctness...) and didactic quality (clarity, comprehensibility, instructiveness...). At the end of each period, each student is anonymously assigned five submitted reports. If the student rates these reports, they can earn one extra point for the period (regardless of whether or not they have submitted a report themselves during the period). This extra point is only given to some students. The criterion is whether the ratings given by the student match those of other students. Thus, students who score more accurately have a higher chance of receiving the extra point, although the influence of chance cannot be ruled out, so it is not recommended to rely on this source of points. In order to receive points for the period, students must submit their evaluation of their classmates' reports. Points are converted to grades at the end of the term as follows: 8-20 pts. = A, 7 pts. = B, 6 pts. = C, 5 pts. = D, 4 pts. = E, 0-3 pts. = F.
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Recommended literature
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Hendl, J. (2012). Přehled statistických metod: analýza a metaanalýza dat. Praha: Portál.
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Martin, P. R., Bateson, P. P. G., & Müller, I. (2009). Úvod do teorie a metodologie měření chování. Praha: Portál.
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Urbánek, T., Denglerová, D., & Širůček, J. (2011). Psychometrika: měření v psychologii. Praha: Portál.
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