HUMB002 Experimental Humanities II

Faculty of Arts
Spring 2016
Extent and Intensity
1/1/0. 5 credit(s). Type of Completion: k (colloquium).
Mgr. Bc. Pavla Linhartová (lecturer)
Mgr. et Mgr. Radek Kundt, Ph.D. (alternate examiner)
Mgr. et Mgr. Eva Kundtová Klocová (lecturer)
Mgr. Čeněk Šašinka, Ph.D. (alternate examiner)
Mgr. Roman Švaříček, Ph.D. (alternate examiner)
Mgr. Alena Holubcová (lecturer)
doc. PhDr. David Zbíral, Ph.D.
Department for the Study of Religions - Faculty of Arts
Contact Person: Mgr. et Mgr. Eva Kundtová Klocová
Supplier department: HUME lab - Experimental Humanities Laboratory - Specialized Units - Faculty of Arts
Wed 17:30–19:05 C31
English language comprehension
Course Enrolment Limitations
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: 5/20, 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
Course objectives
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
  • Specialization Eye-Tracking
    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.
Teaching methods
Lectures, presentations by professionals
Practical/Lab excercises
Preparation of research project/course paper
Presentation of projects and discussion
Assessment methods
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

Statistical analysis:
- 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)
Study Materials
The course is taught annually.
Information on course enrolment limitations: Zápis je podmíněn souhlasem vyučujících.
The course is also listed under the following terms Spring 2017.
  • Enrolment Statistics (Spring 2016, recent)
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