HUMB002 Experimental Humanities II

Filozofická fakulta
jaro 2016
Rozsah
1/1/0. 5 kr. Ukončení: k.
Vyučující
Mgr. Bc. Pavla Linhartová, Ph.D. (přednášející)
Mgr. et Mgr. Radek Kundt, Ph.D. (náhr. zkoušející)
Mgr. et Mgr. Eva Kundtová Klocová, Ph.D. (přednášející)
Mgr. Čeněk Šašinka, Ph.D. (náhr. zkoušející)
Mgr. Roman Švaříček, Ph.D. (náhr. zkoušející)
Mgr. Alena Holubcová (přednášející)
Garance
doc. PhDr. David Zbíral, Ph.D.
Ústav religionistiky - Filozofická fakulta
Kontaktní osoba: Mgr. et Mgr. Eva Kundtová Klocová, Ph.D.
Dodavatelské pracoviště: HUME lab - Laboratoř pro experimentální humanitní vědy - Účelová zařízení - Filozofická fakulta
Rozvrh
St 17:30–19:05 C31
Předpoklady
English language comprehension
Omezení zápisu do předmětu
Předmět je určen pouze studentům mateřských oborů.

Předmět si smí zapsat nejvýše 20 stud.
Momentální stav registrace a zápisu: zapsáno: 0/20, pouze zareg.: 0/20
Jiné omezení: Zápis je podmíněn souhlasem vyučujících.
Mateřské obory
předmět má 19 mateřských oborů, zobrazit
Cíle předmětu
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
Osnova
  • 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.
Výukové metody
Lectures, presentations by professionals
Practical/Lab excercises
Preparation of research project/course paper
Presentation of projects and discussion
Metody hodnocení
Minimum 60 points to pass the course.
Points can be combined from both specializations:

Eye-Tracking:
- 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
Vyučovací jazyk
Angličtina
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích jaro 2017.