The course is only offered to the students of the study fields the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 3/20, only registered: 0/20
Fields of study the course is directly associated with
there are 19 fields of study the course is directly associated with, display
The aim of the course is to prepare students for individual experimental research and data analysis, using hands-on approach and practical excercises in the methods of Eye-Tracking and statistical analysis. Students will be able to organize their curriculum from offered options of specialization, depending on their research orientation.
The students will learn to: Specialization Eye-Tracking
students will: obtain practical and theoretical knowledge in the eye-tracking methodology; learn how to set up the experiment and visualize and analyze the data for three basic types of eye-trackers (towers, remotes and glasses); learn to understand and critique research using Eye-Tracking methodology Specialization Statistical analysis
students will: understand statsitics as a part of research methodology and the elementary statistical concepts used in experimental research; learn to prepare data for analysis, compute elementary statitsics, test elementary hypotheses
Meetings will take place in room B2.12, 9:10 - 10:45, Labs in HUME lab II, 10:50 - 12:25.
22.3. Meeting 1 + Lab 1
29.3. Meeting 2 + Lab 2
5.4. Meeting 3
12.4. Meeting 4
19.4. Meeting 5 + Lab 3
26.4. Meeting 6 (lecture by prof. Kenneth Holmqvist)
Specialization Statistical analysis
Meetings will take place in room G02, 9:10 - 12:25
Meeting 1 (4. 4. 2016)
Lecture: Variables and levels of measurement. Frequencies and distributions. Measures of central tendency and variability.
Practice: Frequencies presentation, distribution interpretation, computing measures of central tendency and variability.
Meeting 2 (11. 4. 2016)
Lecture 1: z-scores and other standardized scores, characteristics of normal distribution.
Practice 1: Computing and interpreting z-scores and other standardized scores.
Lecture 2: Correlation and simple linear regression.
Practice 2: Computing and interpreting correlation and simple linear regression.
Meeting 3 (18. 4. 2016)
Lecture: Statistical induction, confidence intervals, hypothesis testing, significance level,
type I and type II errors.
Practice: Computing confidence intervals and one-sample and independent samples t-tests using Excel.
Meeting 4 (25. 4. 2016)
Test 1 (levels of measurement, frequencies, distributions, measures of central tendency
and variability, z-scores and other standardized scores, correlation, simple linear regression).
Practice 1: Basic SPSS practice (writing data matrix, values and labels, computing and recoding variables, select cases and split file, syntax).
Practice 2: Descriptive statistics (Frequencies, Descriptives and Explore) in SPSS. Finding
and handling mistakes in data and missing data. Correlation in SPSS.
Lecture: Overview of statistical tests, parametric and non-parametric tests.
Meeting 5 (2. 5. 2016)
Test 2 (confidence intervals and hypothesis testing using Excel, choosing appropriate statistical test).
Practice 1: T-tests in SPSS.
Practice 2: Linear regression (multiple and hierarchical) in SPSS.
Meeting 6 (9. 5. 2016)
Lecture: Analysis of variance (ANOVA).
Practice: Practicing ANOVA in SPSS.
Meeting 7 (16. 5. 2016)
Lecture 1: Analysis of categorical data.
Practice 1: Computing chi-square test, categorical data analysis in SPSS.
Lecture 2: Other non-parametric tests.
Practice 2: Other non-parametric tests in SPSS.
Meeting 8 (23. 5. 2016)
Lecture 1: Methodology and statistics. Indicators of effect size. Interpreting significance level and effect sizes. Principles of causality.
Lecture 2: Correct presentation of statistical analysis.
Practice: Practicing analysis in SPSS, figuring out practical problems we can encounter during real data analysis.
Course paper topics assignment.
Lectures, presentations by professionals
Preparation of research project/course paper
Presentation of projects and discussion
Minimum 60 points to pass the course.
Points can be combined from both specializations:
- Attending lectures and Labs (maximum one absence allowed, NOT when your article presentation/criticism is due) = 20 points
- Active participation in discussions, readiness for the articles, sending drafts + comments, = 40 points
- Final project = 40 points
- Meetings consist of theoretical lectures and practical exercises. Maximum one absence is allowed.
- Test 1 = 20 points
- Test 2 = 20 points
- Course paper = 15 points
- Fixal exam = 45 points
Language of instruction
Further comments (probably available only in Czech)
The course is taught annually.
Information on course enrolment limitations: Zápis je podmíněn souhlasem vyučujících.
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