PG22B42 Analysis of Quantitative data

Faculty of Arts
Autumn 2020
Extent and Intensity
0/2/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. Mgr. Martin Sedláček, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Petr Novotný, Ph.D.
Department of Educational Sciences – Faculty of Arts
Contact Person: Ivana Klusáková
Supplier department: Department of Educational Sciences – Faculty of Arts
Timetable
Tue 8:00–9:40 B2.33
Prerequisites (in Czech)
Základní znalost ovládání PC a tabulového procesoru např. MS Excel.
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 10 student(s).
Current registration and enrolment status: enrolled: 0/10, only registered: 0/10, only registered with preference (fields directly associated with the programme): 0/10
Course objectives
The aim of the course is to approach basics of statistical analyzing data acquired from a quantitative survey. Students will be introduced especially to work with statistical sets and variables: creating a set, data navigation and data cleaning, set operations, data translation, creating new variables, case selection and to basic data analysis methods, i.e. to: (1) decomposition of categorical and continuous data and characteristics of this decomposition - univariational analysis; (2) comparison of data allocation and average values of these allocations: t-test, variants analysis; (3) basics of inferential statistics and testing of statistical hypothesis; (4) finding relations between variables and evaluating strength of these relations – bivariational analysis using contingency tables, correlative analysis; (5) finding linear relations between continuous variables: linear regression and scatterplot; (6) data reduction using factor analysis as an attempt to identify factors explaining higher correlation between particular variables (basics of multivariational analysis). After finishing the course, students are able to: demonstrate the basics methods of analyzing data acquired from a quantitative survey.
Syllabus
  • (1) decomposition of categorical and continuous data and characteristics of this decomposition - univariational analysis;
  • (2) comparison of data allocation and average values of these allocations: t-test, variants analysis;
  • (3) basics of inferential statistics and testing of statistical hypothesis;
  • (4) finding relations between variables and evaluating strength of these relations – bivariational analysis using contingency tables, correlative analysis;
  • (5) finding linear relations between continuous variables: linear regression and scatterplot;
  • (6) data reduction using factor analysis as an attempt to identify factors explaining higher correlation between particular variables (basics of multivariational analysis)
Literature
  • HENDL, Jan. Kvalitativní výzkum : základní metody a aplikace. Vyd. 1. Praha: Portál, 2005, 407 s. ISBN 8073670402. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • HENDL, Jan. Přehled statistických metod zpracování dat :analýza a metaanalýza dat. Vyd. 1. Praha: Portál, 2004, 583 s. ISBN 8071788201. info
  • SEBEROVÁ, Helena and Martin SEBERA. Počítačové zpracování dat II. 1. vyd. Vyškov: VVŠ PV, 1999, 134 pp. ISBN 80-7231-052-6. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
Teaching methods
The course is taught as both lectures and seminars. Attendance and participation in the course (min. 75%).
Assessment methods
written test
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Information about innovation of course.
This course has been innovated under the project "Faculty of Arts as Centre of Excellence in Education: Complex Innovation of Study Programmes and Fields at FF MU with Regard to the Requirements of the Knowledge Economy“ – Reg. No. CZ.1.07/2.2.00/28.0228, which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

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The course is also listed under the following terms Spring 2000, Spring 2001, Spring 2002, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Autumn 2006, Autumn 2008, Autumn 2009, Autumn 2010, Spring 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Spring 2020, Spring 2021.
  • Enrolment Statistics (Autumn 2020, recent)
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