PAM126 Analysis of Quantitive Data

Faculty of Arts
Spring 2021
Extent and Intensity
0/2/0. 6 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. Mgr. Martin Sedláček, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Martin Sedláček, Ph.D.
Department of Educational Sciences – Faculty of Arts
Contact Person: doc. Mgr. Martin Sedláček, Ph.D.
Supplier department: Department of Educational Sciences – Faculty of Arts
Timetable
Thu 8:00–9:40 B2.33
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 5 student(s).
Current registration and enrolment status: enrolled: 0/5, only registered: 0/5
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to approach the methods of statistical analyzing data acquired from a quantitative survey. Students will be introduced especially to work with statistical sets and variables, statistical hypothesis testing and the basics of making multilevel models.
Learning outcomes
After finishing the course, students are able to:
- to create a set, data navigation and data cleaning, set operations, data translation, creating new variables, case selection and to basic data analysis methods;
- decompose of categorical and continuous data and characteristics of this decomposition - univariational analysis;
- compare of data allocation and average values of these allocations: t-test, variants analysis;
- apply of basics of inferential statistics and testing of statistical hypothesis;
- find the relations between variables and evaluating strength of these relations – bivariational analysis using contingency tables, correlative analysis;
- understand the linear relations between continuous variables: linear;
- understand the data reduction using factor analysis as an attempt to identify factors explaining higher correlation between particular variables (basics of multivariational analysis);
- critically assess research reports based on statistical data processing.
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
  • MAREŠ, Petr, Ladislav RABUŠIC and Petr SOUKUP. Analýza sociálněvědních dat (nejen) v SPSS (Data analysis in social sciences (using SPSS)). První. Brno: Masarykova univerzita, 2015, 508 pp. ISBN 978-80-210-6362-4. info
  • BABBIE, Earl R. Adventures in social research : data analysis using IBM SPSS statistics. 8th ed. Los Angeles: Sage, 2013, xxiii, 456. ISBN 9781452205588. info
  • FIELD, Andy P. Discovering statistics using IBM SPSS statistics : and sex and drugs and rock 'n' roll. 4th edition. Los Angeles: Sage, 2013, xxxvi, 915. ISBN 9781446249178. info
  • SWEET, Stephen A. Data analysis with SPSS. Boston: Allyn and Bacon, 1999, ix, 204. ISBN 0205265561. 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 each semester.
The course is also listed under the following terms Autumn 2020, Autumn 2021, Spring 2022, Autumn 2022, Spring 2023, Autumn 2023, Spring 2025.
  • Enrolment Statistics (Spring 2021, recent)
  • Permalink: https://is.muni.cz/course/phil/spring2021/PAM126