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.

Entry requirements

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.

Organization of the course 

The course is divided into four modules. The modules DO NOT parallel the weekly units, which are composed of a lecture and a practial. We will start with an introductory meeting on Tuesday, November 2, where you can ask questions about the course, the grading methods, the assignments etc. This introductory meeting will take place instead of the first practical. The first lecture will take place on Thursday, November 4, followed by a practical on November 9, etc. 

The Google Sheet with the teams for the final project is here:
https://docs.google.com/spreadsheets/d/1LUH9nBpdi-JvQhn3GsqcdL8Kg3zLfRZxaNf7HVYvOPA/edit?usp=sharing

Attendance

Attendance is not compulsory, however, it is strongly recommended. The assignments are worked out during the practicals, so you can approach the lecturer with your queries. Moreover, if you choose to work out your assignment before the practicals, you can also sit together with your group members and work on the group project. However, if you feel you're done with everything, you can, of course, leave freely.

Grading

Each weak (with exception of the first and last one), students hand in an assignment. The assignments are published right after the lecture. 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). The deadline for the assignments is 5 minutes before the following lecture, which means Thursday at 15.55.

Late submissions

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. The penalty is not applicable if the student provides a good enough explanation. However, if you submit your assignment after the solutions have been published, you will not be awarded any points.

Communication in the course 

Communication in the course is conducted primarily through the Information System (please note the change). The discussion threads and public announcements (hopefully) ensure 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 link to the discussion forum is here: https://is.muni.cz/auth/discussion/predmetove/fss/podzim2021/PSYn5804/?fakulta=1423;obdobi=8383;predmet=1414741;lang=en

Feel free to start a new thread.

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.

Learning goals

Towards the end of the course, the students should be able to:

• comprehend a scientific article that uses network analysis as a primary analytical method,
• have a decent grasp of the computational and theoretical basis of network analysis,
• 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,
• and, last but not least, independently conduct straightforward network analysis, visualize, and interpret the results.