PSYb2320 R101: A practical guide to using R as your everyday statistical tool

Faculty of Social Studies
Autumn 2019
Extent and Intensity
1/1/0. 4 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Vít Gabrhel (lecturer)
Mgr. Hynek Cígler, Ph.D. (lecturer)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology - Faculty of Social Studies
Contact Person: Mgr. Vít Gabrhel
Supplier department: Department of Psychology - Faculty of Social Studies
Timetable
Mon 18:00–19:40 PC25
Prerequisites
! PSY232 R101: A practical guide to using R as your everyday statistical tool
Any introductory statistics course.
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 15 student(s).
Current registration and enrolment status: enrolled: 6/15, only registered: 0/15
fields of study / plans the course is directly associated with
there are 18 fields of study the course is directly associated with, display
Course objectives
The course has three main goals. The first is to weaken the dependence on paid statistical software that can be unavailable in many future workplaces of our students. The second is to spring interest in a programming language with vast analytical possibilities and a vital global community. The third goal is to refresh statistical foundations from previous courses students may have taken and expand it.
Learning outcomes
Student knows basic principles of the R language and classes of objects it manipulates. Student can work with data, filter it and transform. Student can perform basic statistical analyses and create graphical representations of data and statistics. Models include linear regression, logistic regression, factor analysis and confirmatory factor analysis.
Syllabus
  • R programming language; creating and manipulating data objects; data input and cleaning; elementary data description; comparing means, exploring associations; linear regression; introduction to graphics in R; ANOVA and ANCOVA; categorical data analysis; principal components analysis, factor analysis; confirmatory factor analysis
Literature
  • KLINE, Rex B. Principles and practice of structural equation modeling. Fourth edition. London: The Guilford Press, 2016. xvii, 534. ISBN 9781462523344. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. first published. Los Angeles: Sage, 2012. xxxiv, 957. ISBN 9781446200452. info
Teaching methods
lecture, seminar, reports and discussion
Assessment methods
Credit awarded for individually evaluated R scripts (10x), presentation of selected package, and presence.
Language of instruction
Czech
Further Comments
Study Materials

  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fss/autumn2019/PSYb2320