PSYn5804 Introduction to network analysis

Faculty of Social Studies
Autumn 2021
Extent and Intensity
1/1/0. 4 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Edita Chvojka, MSc (lecturer)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology – Faculty of Social Studies
Supplier department: Department of Psychology – Faculty of Social Studies
Timetable
Tue 2. 11. 16:00–17:40 U23, Tue 9. 11. 16:00–17:40 U23, Tue 16. 11. 16:00–17:40 U23, Tue 23. 11. 16:00–17:40 U23, Tue 30. 11. 16:00–17:40 U23, Tue 7. 12. 16:00–17:40 U23
  • Timetable of Seminar Groups:
PSYn5804/01: Thu 4. 11. 16:00–17:40 U33, Thu 11. 11. 16:00–17:40 U33, Thu 18. 11. 16:00–17:40 U33, Thu 25. 11. 16:00–17:40 U33, Thu 2. 12. 16:00–17:40 U33, Thu 9. 12. 16:00–19:40 U35
Prerequisites
The course aims to be more conceptual and practical than “mathy”. However, basic knowledge of R is strongly recommended. All analyses will be done in R Studio – have it installed before the first lecture (info on this can be found in the study materials). Some knowledge on measurement theories will save you some struggles, as well as a mild background in reflective and formative models. If you want more math, you can always ask the lecturer for some extras.
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 30 student(s).
Current registration and enrolment status: enrolled: 4/30, only registered: 0/30
fields of study / plans the course is directly associated with
Course objectives
Network analysis has gained considerable popularity in psychology over recent years. However, there is more to a network than a pretty picture. A researcher can easily get lost in the plethora of estimators, penalties, and function arguments. Moreover, networks may be pretty, but are they always what researchers really need to answer their research questions? The course has several aims which parallel the weekly modules. One of them is to introduce the students to the theoretical, methodological, and last but not least, philosophical rationale behind network analysis. The other one is to demonstrate basic analytic techniques on different types of data when answering different research questions. The course is concluded by a final project, where groups of students write a short paper that aims to answer a research question that can be investigated using network analysis.
Learning outcomes
Towards the end of the course, the students should be able to:
a) comprehend a scientific article that uses network analysis as a primary analytical method,
b) have a decent grasp of the computational and theoretical basis of network analysis,
c) convincingly argue for choosing network analysis as a method of choice in their own research, but also convincingly argue why network analysis MAY NOT be the preferred way,
d) and, last but not least, independently conduct straightforward network analysis, visualize, and interpret the results.
Syllabus
  • Week 1
  • Practical: None
  • Lecture: What is network analysis?
  • Week 2
  • Practical: What is network analysis?
  • Lecture: Network analysis on the data you want
  • Week 3
  • Practical: Network analysis on the data you want
  • Lecture: Network analysis on the nuisance data
  • Week 4
  • Practical: Network analysis on the nuisance data
  • Lecture: Networks meet the latent trait
  • Week 5
  • Practical: Networks meet the latent trait
  • Lecture: none, Q&A session instead
  • Week 6
  • Group presentations & a pre-Christmas gathering
Literature
    required literature
  • FRIED, E I. Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry. 2020, vol. 31, No 4, p. 271-288. URL info
  • BURGER, J. et al. Reporting Standards for Psychological Network Analyses in Cross-sectional Data. 2020. URL info
Teaching methods
lectures, practicals, discussions, teacher's feedback on assignments, and a pitch presentation with feedback from the lecturer and fellow classmates
Assessment methods
Each weak (with exception of the first and last one), students hand in an assignment. The idea here is that the assignment is worked out individually, without cooperation with the other students. The sum of points for the four individual assignments is worth 45% of the grade. The final group project is worth 55% of the grade. Each submission is graded on a 10-point grading scheme. Students need a minimum of 5.5 to successfully pass the course (be awarded the credits). Should you miss the deadline for the assignment submission, please notify the lecturer and provide an explanation. Late submissions are possible (with a penalty of 2 points/day) before the assignment solutions have been published. However, if you submit your assignment after the solutions have been published, you will not be awarded any points.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
Teacher's information
Communication in the course

Communication in the course is conducted primarily on a Discord server. This medium (hopefully) ensures that the lecturer can answer your queries in a quick and flexible way, and also that all people in the course can learn from the responses. Should you have a private matter to discuss, write the lecturer a PM.

The language of instruction is English. Please keep that in mind and try to refrain from speaking Czech. The course aims to be a safe space for practicing academic English, as well as scientific writing in English.

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