Universität Wien

220050 SE SE Advanced Data Analysis 2 (2023S)

5.00 ECTS (2.00 SWS), SPL 22 - Publizistik- und Kommunikationswissenschaft
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 14.03. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 21.03. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 28.03. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 18.04. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 25.04. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Wednesday 03.05. 13:15 - 14:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 09.05. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 16.05. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 23.05. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 06.06. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 15.06. 11:15 - 12:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Tuesday 20.06. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG
  • Tuesday 27.06. 09:45 - 11:15 Seminarraum 9, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Course Objectives and Summary:

This data analysis seminar focuses on linear regression analysis to explore questions about mediated and moderated effects.

Computer applications will focus on R statistical language, the Rstudio environment (https://www.rstudio.com), and the PROCESS software by Andrew F. Hayes (http://processmacro.org).

The course is subdivided into the following learning units:

• The introductory part of the course will be dedicated to a brief review of the basic principles of linear regression and the setup of R and PROCESS for statistical analysis. We will discuss multiple regression analysis and its principles to understand how to fit, visualize, interpret, and evaluate multiple regression models in R. We will also get a first overview of mediation, moderation, and conditional process models.
• We'll next focus on mediation analysis, seeing how to fit, visualize, interpret, and evaluate mediation models using PROCESS in the R environment.
• The next learning unit will be dedicated to moderation analysis and explaining how to fit, visualize, interpret, and evaluate moderation models using PROCESS in the R environment.
• The final learning unit will be dedicated to an overview of conditional process analysis and explaining how to fit, visualize, interpret, and evaluate conditional process models using PROCESS in the R environment.

The last part of the course will be dedicated to the project work.

By the end of this course, participants will be able to:
• Run and interpret the results of linear regression, moderation, mediation, and conditional process models.
• Know how to test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators.
• Know how to visualize and probe interactions in regression models to interpret interaction effects appropriately.
• Know how to estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects.
• Know how to R language and PROCESS to run, visualize, and understand linear regression, moderation, mediation, and conditional process models.

Assessment and permitted materials

The assessment includes two online quizzes to assess the understanding of key concepts. The results of each questionnaire will constitute 50% of the final grade.

The evaluation also includes a final data analysis project to be conducted in small groups during the course. This constitutes a total of 50% of the final grade.

Minimum requirements and assessment criteria

Continuous participation in class, reading, home study, and exercise are fundamental requirements. In particular, attendance is positively evaluated.

Some knowledge of algebra is assumed to read regression equations and perform basic calculations with variables.

Moderate proficiency in R and R programming is required (the first part of the course will provide a quick hands-on introduction).

Home study and exercise are required to meet the course objectives.

Participants must bring a laptop to class with R and RStudio installed beforehand to follow the coding sessions.

Examination topics

Examination topics consist of the content of the learning units.

Required knowledge and practical skills will be conveyed during the lectures.

The slides used during the lectures will be shared on Moodle.

Additional readings will also be shared on Moodle.

Reading list

The official handbook of the course is:

Andrew F. Hayes. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach. 2018. SECOND EDITION. THE GUILFORD PRESS, New York, London.

Association in the course directory

Last modified: We 07.06.2023 08:47